Offer industrial expertise and strengths in electrical and computer engineering complemented with a passion for research and development with a concentration in analysis of dynamical systems and design of modern/intelligent controllers. Additional research in fuzzy logic and adaptive design for control and modeling complex nonlinear systems. Led the design and build of an intelligent controller for robot and development of software based on FPGA. Thrive on finding optimum solutions to problems. A good eye for identifying interesting areas for investigation, creativity to generate compelling new concepts, and the ability to quickly test and express concepts in the form of functional prototypes. Collaborate effectively with cross-functional teams, other disciplines, and students. Experience reviewing technical papers and editorial board of ‘international journal of control and automation (IJCA), Australia, ISSN: 2005-4297; ‘International Journal of Intelligent System and Applications (IJISA)’, Hong Kong, ISSN:2074-9058; ‘IAES international journal of robotics and automation, Malaysia, ISSN:2089-4856; ‘International Journal of Reconfigurable and Embedded Systems’, Malaysia, ISSN:2089-4864. Address: Ulsan/Korea
Dear Colleagues,
The proper design of controllers for various kinds of systems involving unknown... more Dear Colleagues,
The proper design of controllers for various kinds of systems involving unknown conditions and highly nonlinear and uncertain dynamics remains an open research topic. Meanwhile, machine-learning-based algorithms have been used in several fields, especially when massive amounts of data and great computing power are needed. Research in the field of machine learning aiming to solve issues of flexibility and complexity is ongoing. The connection between (modern) control theory and machine learning is very important in view of surpassing the potentialities of each discipline.
On this note, "control and learning" techniques are presently used in the driving technology underpinning a whole new generation of autonomous devices and cognitive artifacts that, through their learning capabilities, interact seamlessly with the world around them, hence providing the missing link between the digital and physical worlds.
Moreover, control and learning techniques are often used in various industries for control, fault detection, fault diagnosis, and fault-tolerant control. To address these issues, there is a need to develop hybrid algorithms based on control and/or learning; such algorithms can be recommended in this Special Issue.
This Special Issue will focus on control, modeling, various machine learning techniques, fault diagnosis, and fault-tolerant control for systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of various systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to:
Modeling and identification Adaptive and hybrid control Adaptive and hybrid observers Reinforcement learning for control Data-driven control Fault diagnosis Fault-tolerant control of systems based on various control and learning techniques
Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom syst... more Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system's dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenges, this paper proposes a novel technique for fault diagnosis of a rolling-element bearing (REB), founded on a machine-learning-based advanced fuzzy sliding mode observer. First, an ARX-Laguerre algorithm is presented to model the bearing in the presence of noise and uncertainty. In addition, a fuzzy algorithm is applied to the ARX-Laguerre technique to increase the system's modeling accuracy. Next, the conventional sliding mode observer is applied to resolve the problems of fault estimation in a complex system with a high degree of uncertainty, such as rotating machinery. To address the problem of chattering that is inherent in the conventional sliding mode observer, the higher-order super-twisting (advanced) technique is introduced in this study. In addition, the fuzzy method is applied to the advanced sliding mode observer to improve the accuracy of fault estimation in uncertain conditions. As a result, the advanced fuzzy sliding mode observer adaptively improves the reliability, robustness, and estimation accuracy of rolling-element bearing fault estimation. Then, the residual signal delivered by the proposed methodology is split in the windows and each window is characterized by a numerical parameter. Finally, a machine learning technique, called a decision tree, adaptively derives the threshold values that are used for problems of fault detection and fault identification in this study. The effectiveness of the proposed algorithm is validated using a publicly available vibration dataset of Case Western Reverse University. The experimental results show that the machine learning-based advanced fuzzy sliding mode observation methodology significantly improves the reliability and accuracy of the fault estimation, detection, and identification of rolling element bearing faults under variable crack sizes and load conditions.
The design of an effective procedure for leak detection, estimation, and leak size classification... more The design of an effective procedure for leak detection, estimation, and leak size classification is necessary to maintain the healthy and safe operations of pipelines for conveying fluids and gas from one place to another. The complexities of nonlinear and uncertain behavior inherent in a pipeline lead to difficulty of detection, estimation, and leak size estimation. Hence, a robust hybrid leak detection and size estimation method based on the back stepping hyperbolic Takagi-Sugeno (T-S) fuzzy sliding mode extended ARX-Laguerre Proportional Integral (PI) observer for pipelines is presented. Because of the effects of gases and fluids in pipelines, accurate physical modeling of a pipeline is difficult. Consequently, the ARX-Laguerre technique is used for pipeline modeling in this study. Early detection of leaks is important to avoid product loss and other severe damage. To address this issue, the extended ARX-Laguerre PI observer is utilized to detect and estimate a leak. In addition, a T-S fuzzy technique is applied to an extended ARX-Laguerre PI observer to improve leak estimation in the presence of uncertainties. Thus, the T-S fuzzy sliding mode extended ARX-Laguerre PI observer adaptively improves the reliability, robustness, and estimation accuracy of leak detection and estimation. To leak size classification in the presence of uncertainties, the hyperbolic differential equations are governed by the T-S fuzzy extended ARX-Laguerre PI observer to find the exact solution for the kernels of a backstepping-based leak boundary. The leak estimation convergence error shows that the leak size estimation can be calculated independent of the location of the leak, which is the main contribution of this research. It is assumed that pressure and flowmeter sensors are available at the inlet and outlet of the pipeline. The effectiveness of the proposed robust backstepping hyperbolic T-S fuzzy sliding mode extended ARX-Laguerre PI observer was tested over an experimental dataset. According to the results, the proposed technique improved the leak detection, estimation, and size estimation.
Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that a... more Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surgical robot for the sinus is necessary to maintain the high performance and safety necessary for surgery on the maxillary sinus. Thus, a robust adaptive hybrid observation method using an adaptive, fuzzy auto regressive with exogenous input (ARX) Laguerre Takagi-Sugeno (T-S) fuzzy robust feedback linearization observer for a surgical robot is presented. To address the issues of system modeling, the fuzzy ARX-Laguerre technique is represented. In addition, a T-S fuzzy robust feedback linearization observer is applied to a fuzzy ARX-Laguerre to improve the accuracy of fault estimation, reliability, and robustness for the surgical robot in the presence of uncertainties. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy observation-based feedback linearization technique is presented. The effectiveness of the proposed algorithm is tested with simulations. Experimental results show that the proposed method reduces the average position error from 35 mm to 2.45 mm in the presence of faults.
Dear Colleagues,
This Special Issue focuses on mechanics, control, modeling, fault diagnosis, an... more Dear Colleagues,
This Special Issue focuses on mechanics, control, modeling, fault diagnosis, and fault-tolerant control for robotic systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of robotic systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to, the following: modeling, control, fault diagnosis, and fault-tolerant control of robotic systems based on various techniques such as model-based techniques (e.g., sliding mode technique, feedback linearization algorithm, backstepping technique, Lyapunov-based method, etc.), knowledge-based algorithm (e.g., deep learning, transfer learning, fuzzy algorithm, neural network methods, and neuro-fuzzy inference techniques), hybrid methods (e.g., intelligent sliding mode technique, intelligent feedback linearization method, and intelligent backstepping algorithm), and adaptive techniques.
Prof. Dr. Jong-Myon Kim
Dr. Farzin Piltan
Guest Editors
In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy sliding mode extended autoregressive exogeno... more In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy sliding mode extended autoregressive exogenous input (ARX)-Laguerre proportional integral (PI) observer is proposed. The proposed T-S fuzzy sliding mode extended-state ARX-Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection, estimation, and identification. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy sliding mode estimation technique is introduced. The sliding surface slope gain is significant to improve the system's stability, but the sliding mode technique increases high-frequency oscillation (chattering), which reduces the precision of the fault diagnosis and tolerant control. A fuzzy procedure using a sliding surface and actual output position as inputs can adaptively tune the sliding surface slope gain of the sliding mode fault-tolerant control technique. The proposed robust adaptive T-S fuzzy sliding mode estimation extended-state ARX-Laguerre PI observer was verified with six degrees of freedom (DOF) programmable universal manipulation arm (PUMA) 560 robot manipulator, proving qualified efficiency in detecting, isolating, identifying, and tolerant control for faults inherent in sensors and actuators. Experimental results showed that the proposed technique improves the reliability of the fault detection, estimation, identification, and tolerant control.
International Journal of Intelligent Systems and Applications(IJISA), 2019
In practical applications, modeling of real systems with unknown parameters such as distillation ... more In practical applications, modeling of real systems with unknown parameters such as distillation columns are typically complex. To address issues with distillation column estimation, the system is identified by a proposed intelligent, auto-regressive, exogenous-Laguerre (AI-ARX-Laguerre) technique. In this method, an intelligent technique is introduced for data-driven identification of the distillation column. The Laguerre method is used for the removal of input/output noise and decreases the system complexity. The fuzzy logic method is proposed to reduce the system's estimation error and to accurately optimize the ARX-Laguerre parameters. The proposed method outperforms the ARX and ARX-Laguerre technique by achieving average estimation accuracy improvements of 16% and 9%, respectively.
This paper proposes an extended-state ARX-Laguerre proportional integral observer (PIO) for fault... more This paper proposes an extended-state ARX-Laguerre proportional integral observer (PIO) for fault detection and diagnosis (FDD) in bearings. The proposed FDD technique improves fault estimation using a nonlinear function while generating a robust residual signal using the sliding mode technique, which can indirectly improve the performance of FDD. Experimentalresultsindicate that the system modeling error in a healthy condition is less than 2.5×10−10 N.m. In the next step, the ARX-Laguerre PIO is designed to define the state and output of the system observer. The high gain extended-state observer is designed in the third step to estimate the mechanical (bearing) faults based on the nonlinear function. In the last step, robust residual signals are generated based on the sliding mode algorithm for accurate fault identification. This approach improves the performance of an ARX-Laguerre linear PIO method. Employing the proposed method, we demonstrate that in the presence of uncertainties and disturbances, the ball, inner, outer, inner-ball, outer-ball, inner-outer, and inner-outer-ball failures with various motor torque speeds (300 RPM, 400 RPM, 450 RPM, and 500 RPM) and crack sizes (3 mm and 6 mm) are detected, identified, and estimated efficiently. TheeffectivenessoftheproposedtechniqueiscomparedwithanARX-Laguerreproportionalintegral observation (ALPIO). Experimental results indicate that the proposed technique outperforms the ALPIO technique, yielding 17.82% and 16.625% performance improvements for crack sizes of 3 mm and 6 mm, respectively.
The rolling element bearing is a significant component in rotating machinery. Suitable bearing fau... more The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer(FLO).The traditional feedback linearization observer is stable; however, this technique suffers from a lack of robustness. The proposed variable structure technique was used to improve the robustness of the fault estimation while reducing the uncertainties in the feedback linearization observer. The effectiveness of the proposed FLO procedure for the identification of outer, inner, and ball faults was tested using the Case Western University vibration dataset. The proposed model outperformed the variable structure observer (VSO), traditional feedback linearization observer (TFLO), and proportional-integral observer (PIO) by achieving average performance improvements of 5.5%, 8.5%, and 18.5%, respectively.
An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the norm... more An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.
The main contribution of this work is the design of a field programmable gate array (FPGA) based ... more The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.
This paper describes the design of a robust composite high-order super-twisting sliding mode cont... more This paper describes the design of a robust composite high-order super-twisting sliding mode controller (HOSTSMC) for robot manipulators. Robot manipulators are extensively used in industrial manufacturing for many complex and specialized applications. These applications require robots with nonlinear mechanical architectures, resulting in multiple control challenges in various applications. To address this issue, this paper focuses on designing a robust composite high-order super-twisting sliding mode controller by combining a higher-order super-twisting sliding mode controller as the main controller with a super-twisting higher-order sliding mode observer as unknown state measurement and uncertainty estimator in the presence of uncertainty. The proposed method adaptively improves the traditional sliding mode controller (TSMC) and the estimated state sliding mode controller (ESMC) to attenuate the chattering. The effectiveness of a HOSTSMC is tested over six degrees of freedom (DOF) using a Programmable Universal Manipulation Arm (PUMA) robot manipulator. The proposed method outperforms the TSMC and ESMC, yielding 4.9% and 2% average performance improvements in the output position root-mean-square (RMS) error and average error, respectively. e
—The main contribution of this paper is the design of a robust model reference fuzzy sliding mode... more —The main contribution of this paper is the design of a robust model reference fuzzy sliding mode observation technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. A fuzzy sliding mode controller was used in this study to control the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, chattering phenomenon, and error convergence under uncertain conditions, the proposed sliding mode observer was applied to the fuzzy sliding mode controller. This theory was applied to a six-degrees-of-freedom (DOF) PUMA robot manipulator to verify the power of the proposed method.
Internatinal Journal of Control and Automation, 2018
The main objective of this paper is the design of a robust and stable multivariable decoupling ba... more The main objective of this paper is the design of a robust and stable multivariable decoupling based Proportional-Integral-Derivative (PID) like fuzzy scheduling technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical distillation column. A PID like fuzzy scheduling controller was used in this paper to control the distillation column in the presence of uncertainty and disturbance. To address the challenges of robustness, stability, and error convergence under uncertain conditions, the proposed multivariable decupling method was applied to the PID like fuzzy scheduling method. This theory was applied to a P-canonical form and V-canonical form of distillation column modeling to verify the power of the control, stability and robustness proposed method.
System Identification is used to build
mathematical models of a dynamic system based on
measured ... more System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algorithms. In this research, two important types algorithm are compared to identifying the highly nonlinear systems, namely: Auto- Regressive with eXternal model input (ARX) and Auto Regressive moving Average with eXternal model input (Armax) Theory. These two methods are applied to the highly nonlinear industrial motor.
System identification is one of the main challenges in real time control. To design the best cont... more System identification is one of the main challenges in real time control. To design the best controller for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. The second important challenge in the field of control theory is, design high-performance controller. To improve the performance of controller, two factors are very important: 1) high performance mathematical or intelligent modeling, 2) chose the best controller for the system. This paper has two main objectives: after data collection from position motor from industry the first objective is modeling and system identification based on Auto-Regressive with eXternal model input (ARX) and defined Z-function and S-function and the second objective is; design the high-performance controller to have the minimum rise time and error.
One of the most familiar challenge of air pollution over the cities is smog hanging. The effects ... more One of the most familiar challenge of air pollution over the cities is smog hanging. The effects of inhaling particulate matter have been studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. There are, however, some additional products of the combustion process that include nitrogen oxides and sulfur and some un-combusted hydrocarbons, depending on the operating conditions and the fuel-air ratio. one of the important parameters to the control of lung cancer in big cities around the world is tuning the fuel to air ratio. To tuning the fuel to air ratio, functional based nonlinear controller is introduced. A mathematical function is used to improve the performance of the tuning the fuel to air ratio.
In this research, Neuro-fuzzy fuzzy feedback linearization controller is recommended for sensitiv... more In this research, Neuro-fuzzy fuzzy feedback linearization controller is recommended for sensitive three degrees of freedom dental actuator. To design stable high quality controller conventional feedback linearization controller is recommended. Conventional feedback linearization (FL) controller is a nonlinear, stable, and reliable controller. This controller is model-base and in uncertain situation, model-base is challenge. The nonlinear dynamic formulation problem in highly nonlinear system has been solved by fuzzy logic theorem. Fuzzy logic theory is used to estimate the system dynamics. This type of controller is free of mathematical dynamic parameters of plant. When system works in uncertainty, the nonlinearity term of system is not equal to equivalent term of FL controller. According to this technique, the number of rule base is reduced with respect to have PID like fuzzy logic controller. The simulation results show that the proposed controller works well in various situations. Based on result and discussion, proposed method can eliminate chattering in certain and uncertain condition. This controller reduces the level of energy due to the torque performance. This controller removed the fluctuation in presence of uncertainties.
Medical robots are sensitive tools to improve the surgery's performance. One of the most active r... more Medical robots are sensitive tools to improve the surgery's performance. One of the most active research area in this field is control of medical robot. In this research, nonlinear, stable and robust Sliding Mode controller (SMC) is used as a based controller. This algorithm works based on the functional operation. The main traditional functions for this algorithm are switching and saturation functions. In this research, fuzzy algorithm is used to design a unique function to adjust the output performance. According to this research, the chattering eliminated based on applied modified sliding function, which is more robust than conventional sliding mode controller.
Design a robust oscillation-free controller for multi input-multi output (MIMO) nonlinear uncerta... more Design a robust oscillation-free controller for multi input-multi output (MIMO) nonlinear uncertain dynamical system (sensitive dental joint) is the main objective in this research. In this paper, robust sliding mode controller will be selected as a main control technique and linear controller will be design to improve the stability and robustness to control of dental joint. The proposed approach effectively combines of design methods from switching sliding mode controller, and linear Proportional-Integral-Derivative (PID) control to improve the performance, stability and robustness of the sliding mode controller. Conventional sliding mode controller has two important subparts, switching and equivalent. Switching part (discontinuous part) is very important in uncertain condition but it causes chattering phenomenon. To solve the chattering, the most common method used is linear boundary layer saturation method, but this method lost the stability. To reduce the chattering with respect to stability and robustness; linear controller is added to the switching part of the sliding mode controller. The linear controller is to reduce the role of sliding surface slope and switching (sign) function. This controller improves the stability and robustness, reduces the chattering as well and reduces the level of energy due to the torque performance as well.
Dear Colleagues,
The proper design of controllers for various kinds of systems involving unknown... more Dear Colleagues,
The proper design of controllers for various kinds of systems involving unknown conditions and highly nonlinear and uncertain dynamics remains an open research topic. Meanwhile, machine-learning-based algorithms have been used in several fields, especially when massive amounts of data and great computing power are needed. Research in the field of machine learning aiming to solve issues of flexibility and complexity is ongoing. The connection between (modern) control theory and machine learning is very important in view of surpassing the potentialities of each discipline.
On this note, "control and learning" techniques are presently used in the driving technology underpinning a whole new generation of autonomous devices and cognitive artifacts that, through their learning capabilities, interact seamlessly with the world around them, hence providing the missing link between the digital and physical worlds.
Moreover, control and learning techniques are often used in various industries for control, fault detection, fault diagnosis, and fault-tolerant control. To address these issues, there is a need to develop hybrid algorithms based on control and/or learning; such algorithms can be recommended in this Special Issue.
This Special Issue will focus on control, modeling, various machine learning techniques, fault diagnosis, and fault-tolerant control for systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of various systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to:
Modeling and identification Adaptive and hybrid control Adaptive and hybrid observers Reinforcement learning for control Data-driven control Fault diagnosis Fault-tolerant control of systems based on various control and learning techniques
Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom syst... more Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system's dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenges, this paper proposes a novel technique for fault diagnosis of a rolling-element bearing (REB), founded on a machine-learning-based advanced fuzzy sliding mode observer. First, an ARX-Laguerre algorithm is presented to model the bearing in the presence of noise and uncertainty. In addition, a fuzzy algorithm is applied to the ARX-Laguerre technique to increase the system's modeling accuracy. Next, the conventional sliding mode observer is applied to resolve the problems of fault estimation in a complex system with a high degree of uncertainty, such as rotating machinery. To address the problem of chattering that is inherent in the conventional sliding mode observer, the higher-order super-twisting (advanced) technique is introduced in this study. In addition, the fuzzy method is applied to the advanced sliding mode observer to improve the accuracy of fault estimation in uncertain conditions. As a result, the advanced fuzzy sliding mode observer adaptively improves the reliability, robustness, and estimation accuracy of rolling-element bearing fault estimation. Then, the residual signal delivered by the proposed methodology is split in the windows and each window is characterized by a numerical parameter. Finally, a machine learning technique, called a decision tree, adaptively derives the threshold values that are used for problems of fault detection and fault identification in this study. The effectiveness of the proposed algorithm is validated using a publicly available vibration dataset of Case Western Reverse University. The experimental results show that the machine learning-based advanced fuzzy sliding mode observation methodology significantly improves the reliability and accuracy of the fault estimation, detection, and identification of rolling element bearing faults under variable crack sizes and load conditions.
The design of an effective procedure for leak detection, estimation, and leak size classification... more The design of an effective procedure for leak detection, estimation, and leak size classification is necessary to maintain the healthy and safe operations of pipelines for conveying fluids and gas from one place to another. The complexities of nonlinear and uncertain behavior inherent in a pipeline lead to difficulty of detection, estimation, and leak size estimation. Hence, a robust hybrid leak detection and size estimation method based on the back stepping hyperbolic Takagi-Sugeno (T-S) fuzzy sliding mode extended ARX-Laguerre Proportional Integral (PI) observer for pipelines is presented. Because of the effects of gases and fluids in pipelines, accurate physical modeling of a pipeline is difficult. Consequently, the ARX-Laguerre technique is used for pipeline modeling in this study. Early detection of leaks is important to avoid product loss and other severe damage. To address this issue, the extended ARX-Laguerre PI observer is utilized to detect and estimate a leak. In addition, a T-S fuzzy technique is applied to an extended ARX-Laguerre PI observer to improve leak estimation in the presence of uncertainties. Thus, the T-S fuzzy sliding mode extended ARX-Laguerre PI observer adaptively improves the reliability, robustness, and estimation accuracy of leak detection and estimation. To leak size classification in the presence of uncertainties, the hyperbolic differential equations are governed by the T-S fuzzy extended ARX-Laguerre PI observer to find the exact solution for the kernels of a backstepping-based leak boundary. The leak estimation convergence error shows that the leak size estimation can be calculated independent of the location of the leak, which is the main contribution of this research. It is assumed that pressure and flowmeter sensors are available at the inlet and outlet of the pipeline. The effectiveness of the proposed robust backstepping hyperbolic T-S fuzzy sliding mode extended ARX-Laguerre PI observer was tested over an experimental dataset. According to the results, the proposed technique improved the leak detection, estimation, and size estimation.
Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that a... more Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surgical robot for the sinus is necessary to maintain the high performance and safety necessary for surgery on the maxillary sinus. Thus, a robust adaptive hybrid observation method using an adaptive, fuzzy auto regressive with exogenous input (ARX) Laguerre Takagi-Sugeno (T-S) fuzzy robust feedback linearization observer for a surgical robot is presented. To address the issues of system modeling, the fuzzy ARX-Laguerre technique is represented. In addition, a T-S fuzzy robust feedback linearization observer is applied to a fuzzy ARX-Laguerre to improve the accuracy of fault estimation, reliability, and robustness for the surgical robot in the presence of uncertainties. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy observation-based feedback linearization technique is presented. The effectiveness of the proposed algorithm is tested with simulations. Experimental results show that the proposed method reduces the average position error from 35 mm to 2.45 mm in the presence of faults.
Dear Colleagues,
This Special Issue focuses on mechanics, control, modeling, fault diagnosis, an... more Dear Colleagues,
This Special Issue focuses on mechanics, control, modeling, fault diagnosis, and fault-tolerant control for robotic systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of robotic systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to, the following: modeling, control, fault diagnosis, and fault-tolerant control of robotic systems based on various techniques such as model-based techniques (e.g., sliding mode technique, feedback linearization algorithm, backstepping technique, Lyapunov-based method, etc.), knowledge-based algorithm (e.g., deep learning, transfer learning, fuzzy algorithm, neural network methods, and neuro-fuzzy inference techniques), hybrid methods (e.g., intelligent sliding mode technique, intelligent feedback linearization method, and intelligent backstepping algorithm), and adaptive techniques.
Prof. Dr. Jong-Myon Kim
Dr. Farzin Piltan
Guest Editors
In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy sliding mode extended autoregressive exogeno... more In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy sliding mode extended autoregressive exogenous input (ARX)-Laguerre proportional integral (PI) observer is proposed. The proposed T-S fuzzy sliding mode extended-state ARX-Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection, estimation, and identification. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy sliding mode estimation technique is introduced. The sliding surface slope gain is significant to improve the system's stability, but the sliding mode technique increases high-frequency oscillation (chattering), which reduces the precision of the fault diagnosis and tolerant control. A fuzzy procedure using a sliding surface and actual output position as inputs can adaptively tune the sliding surface slope gain of the sliding mode fault-tolerant control technique. The proposed robust adaptive T-S fuzzy sliding mode estimation extended-state ARX-Laguerre PI observer was verified with six degrees of freedom (DOF) programmable universal manipulation arm (PUMA) 560 robot manipulator, proving qualified efficiency in detecting, isolating, identifying, and tolerant control for faults inherent in sensors and actuators. Experimental results showed that the proposed technique improves the reliability of the fault detection, estimation, identification, and tolerant control.
International Journal of Intelligent Systems and Applications(IJISA), 2019
In practical applications, modeling of real systems with unknown parameters such as distillation ... more In practical applications, modeling of real systems with unknown parameters such as distillation columns are typically complex. To address issues with distillation column estimation, the system is identified by a proposed intelligent, auto-regressive, exogenous-Laguerre (AI-ARX-Laguerre) technique. In this method, an intelligent technique is introduced for data-driven identification of the distillation column. The Laguerre method is used for the removal of input/output noise and decreases the system complexity. The fuzzy logic method is proposed to reduce the system's estimation error and to accurately optimize the ARX-Laguerre parameters. The proposed method outperforms the ARX and ARX-Laguerre technique by achieving average estimation accuracy improvements of 16% and 9%, respectively.
This paper proposes an extended-state ARX-Laguerre proportional integral observer (PIO) for fault... more This paper proposes an extended-state ARX-Laguerre proportional integral observer (PIO) for fault detection and diagnosis (FDD) in bearings. The proposed FDD technique improves fault estimation using a nonlinear function while generating a robust residual signal using the sliding mode technique, which can indirectly improve the performance of FDD. Experimentalresultsindicate that the system modeling error in a healthy condition is less than 2.5×10−10 N.m. In the next step, the ARX-Laguerre PIO is designed to define the state and output of the system observer. The high gain extended-state observer is designed in the third step to estimate the mechanical (bearing) faults based on the nonlinear function. In the last step, robust residual signals are generated based on the sliding mode algorithm for accurate fault identification. This approach improves the performance of an ARX-Laguerre linear PIO method. Employing the proposed method, we demonstrate that in the presence of uncertainties and disturbances, the ball, inner, outer, inner-ball, outer-ball, inner-outer, and inner-outer-ball failures with various motor torque speeds (300 RPM, 400 RPM, 450 RPM, and 500 RPM) and crack sizes (3 mm and 6 mm) are detected, identified, and estimated efficiently. TheeffectivenessoftheproposedtechniqueiscomparedwithanARX-Laguerreproportionalintegral observation (ALPIO). Experimental results indicate that the proposed technique outperforms the ALPIO technique, yielding 17.82% and 16.625% performance improvements for crack sizes of 3 mm and 6 mm, respectively.
The rolling element bearing is a significant component in rotating machinery. Suitable bearing fau... more The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer(FLO).The traditional feedback linearization observer is stable; however, this technique suffers from a lack of robustness. The proposed variable structure technique was used to improve the robustness of the fault estimation while reducing the uncertainties in the feedback linearization observer. The effectiveness of the proposed FLO procedure for the identification of outer, inner, and ball faults was tested using the Case Western University vibration dataset. The proposed model outperformed the variable structure observer (VSO), traditional feedback linearization observer (TFLO), and proportional-integral observer (PIO) by achieving average performance improvements of 5.5%, 8.5%, and 18.5%, respectively.
An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the norm... more An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.
The main contribution of this work is the design of a field programmable gate array (FPGA) based ... more The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.
This paper describes the design of a robust composite high-order super-twisting sliding mode cont... more This paper describes the design of a robust composite high-order super-twisting sliding mode controller (HOSTSMC) for robot manipulators. Robot manipulators are extensively used in industrial manufacturing for many complex and specialized applications. These applications require robots with nonlinear mechanical architectures, resulting in multiple control challenges in various applications. To address this issue, this paper focuses on designing a robust composite high-order super-twisting sliding mode controller by combining a higher-order super-twisting sliding mode controller as the main controller with a super-twisting higher-order sliding mode observer as unknown state measurement and uncertainty estimator in the presence of uncertainty. The proposed method adaptively improves the traditional sliding mode controller (TSMC) and the estimated state sliding mode controller (ESMC) to attenuate the chattering. The effectiveness of a HOSTSMC is tested over six degrees of freedom (DOF) using a Programmable Universal Manipulation Arm (PUMA) robot manipulator. The proposed method outperforms the TSMC and ESMC, yielding 4.9% and 2% average performance improvements in the output position root-mean-square (RMS) error and average error, respectively. e
—The main contribution of this paper is the design of a robust model reference fuzzy sliding mode... more —The main contribution of this paper is the design of a robust model reference fuzzy sliding mode observation technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. A fuzzy sliding mode controller was used in this study to control the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, chattering phenomenon, and error convergence under uncertain conditions, the proposed sliding mode observer was applied to the fuzzy sliding mode controller. This theory was applied to a six-degrees-of-freedom (DOF) PUMA robot manipulator to verify the power of the proposed method.
Internatinal Journal of Control and Automation, 2018
The main objective of this paper is the design of a robust and stable multivariable decoupling ba... more The main objective of this paper is the design of a robust and stable multivariable decoupling based Proportional-Integral-Derivative (PID) like fuzzy scheduling technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical distillation column. A PID like fuzzy scheduling controller was used in this paper to control the distillation column in the presence of uncertainty and disturbance. To address the challenges of robustness, stability, and error convergence under uncertain conditions, the proposed multivariable decupling method was applied to the PID like fuzzy scheduling method. This theory was applied to a P-canonical form and V-canonical form of distillation column modeling to verify the power of the control, stability and robustness proposed method.
System Identification is used to build
mathematical models of a dynamic system based on
measured ... more System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algorithms. In this research, two important types algorithm are compared to identifying the highly nonlinear systems, namely: Auto- Regressive with eXternal model input (ARX) and Auto Regressive moving Average with eXternal model input (Armax) Theory. These two methods are applied to the highly nonlinear industrial motor.
System identification is one of the main challenges in real time control. To design the best cont... more System identification is one of the main challenges in real time control. To design the best controller for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. The second important challenge in the field of control theory is, design high-performance controller. To improve the performance of controller, two factors are very important: 1) high performance mathematical or intelligent modeling, 2) chose the best controller for the system. This paper has two main objectives: after data collection from position motor from industry the first objective is modeling and system identification based on Auto-Regressive with eXternal model input (ARX) and defined Z-function and S-function and the second objective is; design the high-performance controller to have the minimum rise time and error.
One of the most familiar challenge of air pollution over the cities is smog hanging. The effects ... more One of the most familiar challenge of air pollution over the cities is smog hanging. The effects of inhaling particulate matter have been studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. There are, however, some additional products of the combustion process that include nitrogen oxides and sulfur and some un-combusted hydrocarbons, depending on the operating conditions and the fuel-air ratio. one of the important parameters to the control of lung cancer in big cities around the world is tuning the fuel to air ratio. To tuning the fuel to air ratio, functional based nonlinear controller is introduced. A mathematical function is used to improve the performance of the tuning the fuel to air ratio.
In this research, Neuro-fuzzy fuzzy feedback linearization controller is recommended for sensitiv... more In this research, Neuro-fuzzy fuzzy feedback linearization controller is recommended for sensitive three degrees of freedom dental actuator. To design stable high quality controller conventional feedback linearization controller is recommended. Conventional feedback linearization (FL) controller is a nonlinear, stable, and reliable controller. This controller is model-base and in uncertain situation, model-base is challenge. The nonlinear dynamic formulation problem in highly nonlinear system has been solved by fuzzy logic theorem. Fuzzy logic theory is used to estimate the system dynamics. This type of controller is free of mathematical dynamic parameters of plant. When system works in uncertainty, the nonlinearity term of system is not equal to equivalent term of FL controller. According to this technique, the number of rule base is reduced with respect to have PID like fuzzy logic controller. The simulation results show that the proposed controller works well in various situations. Based on result and discussion, proposed method can eliminate chattering in certain and uncertain condition. This controller reduces the level of energy due to the torque performance. This controller removed the fluctuation in presence of uncertainties.
Medical robots are sensitive tools to improve the surgery's performance. One of the most active r... more Medical robots are sensitive tools to improve the surgery's performance. One of the most active research area in this field is control of medical robot. In this research, nonlinear, stable and robust Sliding Mode controller (SMC) is used as a based controller. This algorithm works based on the functional operation. The main traditional functions for this algorithm are switching and saturation functions. In this research, fuzzy algorithm is used to design a unique function to adjust the output performance. According to this research, the chattering eliminated based on applied modified sliding function, which is more robust than conventional sliding mode controller.
Design a robust oscillation-free controller for multi input-multi output (MIMO) nonlinear uncerta... more Design a robust oscillation-free controller for multi input-multi output (MIMO) nonlinear uncertain dynamical system (sensitive dental joint) is the main objective in this research. In this paper, robust sliding mode controller will be selected as a main control technique and linear controller will be design to improve the stability and robustness to control of dental joint. The proposed approach effectively combines of design methods from switching sliding mode controller, and linear Proportional-Integral-Derivative (PID) control to improve the performance, stability and robustness of the sliding mode controller. Conventional sliding mode controller has two important subparts, switching and equivalent. Switching part (discontinuous part) is very important in uncertain condition but it causes chattering phenomenon. To solve the chattering, the most common method used is linear boundary layer saturation method, but this method lost the stability. To reduce the chattering with respect to stability and robustness; linear controller is added to the switching part of the sliding mode controller. The linear controller is to reduce the role of sliding surface slope and switching (sign) function. This controller improves the stability and robustness, reduces the chattering as well and reduces the level of energy due to the torque performance as well.
The main reasons to use fuzzy logic technology are ability to give approximate recommended solvin... more The main reasons to use fuzzy logic technology are ability to give approximate recommended solving unclear and complex problem, easy to understand, and flexible then a designer is able to model controller for any nonlinear plant with a set of IF-THEN rules, or it can identify the control actions and describe them by using fuzzy rules. It must be noted that application of fuzzy logic is not limited to a system that difficult for modeling, but it can be used in clear systems that have complex mathematics models because most of time it can be shortened in design, but the quality of design may not always be so high.
In this research based center (IRAN SSP) we can help you to improve your academic C.V for educati... more In this research based center (IRAN SSP) we can help you to improve your academic C.V for education and work. after this research course you can to extract at least 4-6 papers from projects. The main question is how?????
Contact US: piltan_f@iranssp.com Price: 1500 USD
Time: 200 hours
"In this project course we have following objectives:
1) intro to MATLAB/SIMULINK
2) intro to 3... more "In this project course we have following objectives:
1) intro to MATLAB/SIMULINK
2) intro to 3-D motor and extract the system dynamics and kinematics from impact journal papers
3)implement dynamic and kinematics of 3-D motor using MATLAB/SIMULINK.
4)intro to linear and nonlinear controllers, compare between all types of controller
5) Design linear, conventional nonlinear and fuzzy controller for 3-D motor and compare them to select the best controller.
6)modify the select control method based on intelligence method and conventional control methodology
7)test and result discussion
8) extract journal papers and send to indexed journal"
Objectives in this research project are:
1)To modelling and implementation of robot manipulator ... more Objectives in this research project are:
1)To modelling and implementation of robot manipulator based on paper using Matlab/simulink
2) Design a linear and nonlinear controller
3)Test and result
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of
t... more Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of
the most important challenging works. This course focuses on the design, implementation and
analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot
manipulator and compare to computed torque controller, in presence of uncertainties. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.)
research center, Shiraz, Iran.
In this research, a position sliding mode controller using FPGA design and application to robotic... more In this research, a position sliding mode controller using FPGA design and application to robotic manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller and FPGA based the output has improved. Sliding mode methodology by adding to the FPGA based has covered negative points. The main target in this research is modeling, analyses and design of the position FPGA-based controller for robot manipulator to reach an acceptable performance. Robot manipulators are nonlinear, and a number of parameters are uncertain, this research focuses on modelling as accurate as possible using both analytical paradigms and the advantages and sliding mode controller and sliding mode controller using FPGA to select the best controller for the industrial manipulator. Although sliding mode controller has acceptable performance with known dynamic parameters such as stability and robustness but there are two important disadvantages as below: chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. This controller works very well in certain environment but if it works in uncertain area or various dynamic parameters, it has slight chattering phenomenon. The system performance in sliding mode controller is sensitive to the sliding surface sloop. Therefore, compute the optimum value of sliding surface slope for a system is important challenge work. This problem has solved by adjusting surface slope in real-time. In this way, the overall system performance has improved with respect to the classical sliding mode controller. This controller solved chattering phenomenon as by applied saturation function method and tuning the sliding surface slope. By comparing between sliding mode controller and sliding mode controller using FPGA, found that both of method have steadily stabilised in output response (e.g., torque
2
performance) but they have slight chattering in the presence of uncertainties. Sliding mode controller and application in FPGA has many advantages such as high speed, low cost, short time to market and small device size. FPGA can be used to design a controller in a single chip Integrated Circuit (IC). In this research the SMC is designed using VHDL language for implementation on FPGA device (XA3S1600E-Spartan-3E), with minimum chattering and high processing speed (63.29 MHz).
An industrial robot is defined by ISO 8373 as an automatically controlled, reprogrammable, multip... more An industrial robot is defined by ISO 8373 as an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes. The field of robotics may be more practically defined as the study, design and use of robot systems for manufacturing (a top-level definition relying on the prior definition of robot). Typical applications of robots include welding, painting, assembly, pick and place (such as packaging, palletizing and SMT), product inspection, aerospace, and testing; all accomplished with high endurance, speed, and precision. To have precision type of controller played an important role. This project focuses on the design, implementation and analysis of a high quality nonlinear controller for industrial PUMA robot manipulator, in presence of uncertainties. In this design sliding mode controller is the best candidate. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has two important challenges
1. Chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers
2. Dependence of the nonlinear dynamic formulation
To solve the first challenge linear function was introduced, because this method could reduce the high frequency oscillation. To solve the second challenge in this project intelligent methodology is introduced.
Iranian Institute of Advance Science and Technology (IRAN-SSP)
Pollution is the introduction of contaminants into the natural environment that cause adverse cha... more Pollution is the introduction of contaminants into the natural environment that cause adverse change. Pollution can take the form of chemical substances or energy, such as noise, heat or light. Pollutants, the components of pollution, can be either foreign substances/energies or naturally occurring contaminants. Pollution is often classed as point source or nonpoint source pollution. Nonpoint source (NPS) pollution refers to both water and air pollution from diffuse sources. Nonpoint source air pollution affects air quality from sources such as smokestacks or car tailpipes. Most automobiles in use today are propelled by an internal combustion engine, fueled by deflagration of gasoline (also known as petrol) or diesel. Both fuels are known to cause air pollution and are also blamed for contributing to climate change and global warming. Rapidly increasing oil prices, concerns about oil dependence, tightening environmental laws and restrictions on greenhouse gas emissions are propelling work on alternative power systems or increase the efficiency for automobiles. Internal combustion engines produce air pollution emissions, due to incomplete combustion of carbonaceous fuel. The effects of inhaling particulate matter have been studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. There are, however, some additional products of the combustion process that include nitrogen oxides and sulfur and some un-combusted hydrocarbons, depending on the operating conditions and the fuel-air ratio. In this research intelligent tuning the rate of fuel-air ratio is analyzed.
Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, multi inputs-multi outputs and time variant. One purpose of accurate modeling is to save development costs of real engines and minimizing the risks of damaging an engine when validating controller designs. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity.
An intelligent nonlinear estimator with varying parameter gain is designed with guaranteed stability to allow implementation of the proposed state nonlinear methodology, where the nonlinear control strategy is implemented into a model engine control module. To estimate the dynamic model of IC engine fuzzy inference engine is applied to pure nonlinear control. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired fuel-air ratio and fuel mass over a given time interval.
Iranian Institute of advance Science and Technology (IRAN-SSP)
A Continuum robot manipulator is a slender hyper-redundant manipulator. The high number of degre... more A Continuum robot manipulator is a slender hyper-redundant manipulator. The high number of degrees of freedom allows the arm to “snake” along a path or around an obstacle – hence the name “snake-arm”. The ability to reach into confined spaces lends itself to many applications involving access problems. Continuum robots are used in many applications such as; industry, nuclear, aerospace, automotive, security, and robotic surgery.
Robotic surgery, computer-assisted surgery, and robotically-assisted surgery are terms for technological developments that use robotic systems to aid in surgical procedures. Robotically-assisted surgery was developed to overcome the limitations of minimally-invasive surgery and to enhance the capabilities of surgeons performing open surgery.
Major advances aided by surgical robots have been remote surgery, minimally invasive surgery and unmanned surgery. Due to robotic use, the surgery is done with precision, miniaturization, smaller incisions; decreased blood loss, less pain, and quicker healing time. Articulation beyond normal manipulation and three-dimensional magnification helps resulting in improved ergonomics. Due to these techniques there is a reduced duration of hospital stays, blood loss, transfusions, and use of pain medication. The existing open surgery technique has many flaws like limited access to surgical area, long recovery time, long hours of operation, blood loss, surgical scars and marks.
Compared with other minimally invasive surgery approaches, robot-assisted surgery gives the surgeon better control over the surgical instruments and a better view of the surgical site. In addition, surgeons no longer have to stand throughout the surgery and do not tire as quickly. Naturally, the surgical robot can continuously be used by rotating surgery teams. Finally, occurring hand tremors are filtered out by the robot’s controllers.
This research contributes to the filtering the hand tremors in continuum robot manipulator based on new technical methodology. Intelligent-Backstepping methodology is selected to reduce the hand tremors in continuum robot. This research addresses two basic issues related to the filtering hand tremors in continuum robots; (1) a more accurate representation of the dynamic model of an existing prototype, and (2) the design of a robust nonlinear filter. The nonlinear filter developed in this research is designed into two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation. Next, the artificial intelligence methodologies applied to it to solution uncertainty problem and reduce the hand tremors. Two factors are important in this research: system flexibility and time of response. To improve these two factors new methodology of intelligent-backstepping method is introduced.
Iranian Institute of Advance Science and Technology (IRAN SSP)
Multi-degree-of-freedom (DOF) actuators are finding wide use in a number of Industries (such as a... more Multi-degree-of-freedom (DOF) actuators are finding wide use in a number of Industries (such as aerospace, automotive industry and surgical robot). Currently, a significant number of the existing robotic actuators that can realize multi-DOF motion are constructed using gear and linkages to connect several single-DOF motors in series and/or parallel. Not only do such actuators tend to be large in size and mass, but they also have a decreased positioning accuracy due to mechanical deformation, friction and backlash of the gears and linkages. A number of these systems also exhibit singularities in their workspaces, which makes it virtually impossible to obtain uniform, high-speed, and high-precision motion.
For high precession trajectory planning and control, it is necessary to replace the actuator system made up of several single-DOF motors connected in series and/or parallel with a single multi-DOF actuator. The need for such systems has motivated years of research in the development of unusual, yet high performance actuators that have the potential to realize multi-DOF motion in a single joint. One such actuator is the spherical motor.
Compared to conventional robotic manipulators that offer the same motion capabilities, the spherical motor possesses several advantages. Not only can the motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace. The spherical motor is much simpler and more compact in design than most multiple single-axis robotic manipulators. The motor is also relatively easy to manufacture. The spherical motor have potential contributions to a wide range of applications such as coordinate measuring, object tracking, material handling, automated assembling, welding, and laser cutting. All these applications require high precision motion and fast dynamic response, which the spherical motor is capable of delivering. Previous research efforts on the spherical motor have demonstrated most of these features. These, however, come with a number of challenges. The spherical motor exhibits coupled, nonlinear and very complex dynamics. The design and implementation of feedback controllers for the motor are complicated by these dynamics. The controller design is further complicated by the orientation-varying torque generated by the spherical motor.
An Important question which comes to mind is that why this proposed methodology should be used when lots of control techniques are accessible to design high precision motion and fast dynamic response?
Answering to this question is the main objective in this part.
The dynamics of a spherical motor is highly nonlinear, time variant, MIMO, uncertain and there exist strong coupling effects between joints.
Spherical motor’s dynamic models through a large number of highly nonlinear parameters generate the problem of computation as a result it is caused to many challenges for real¬ time applications. To eliminate the actual acceleration measurement and also the computation burden as well as have stabile, efficiency and robust controller, base-line sliding mode controller is introduced. Assuming unstructured uncertainties and structure uncertainties can be defined into one term and considered as an uncertainty and external disturbance, the problem of computation burden and large number of parameters can be solved to some extent. Hence conventional switching sliding mode controller is an apparent nominates to design a controller using the bounds of the uncertainties and external disturbance. There are three main issues limiting the applications of conventional sliding mode controller; dynamic-based formulation of conventional control method, computation of the bounds of uncertainties, and high frequency oscillation. The problem in time of system response dynamic formulation of spherical motor is not a simple task and minimum rule-base fuzzy logic theory is used to reduce this challenge. If PID fuzzy logic controller is used, we have limitation in the number of fuzzy rule table. Consequently in this design PID fuzzy is extract by PD fuzzy plus PI fuzzy theory. To have a good design, PID fuzzy logic controller will be work based on PD fuzzy rule table in three parts of fuzzy logic area.
Uncertainties are very important challenges and caused to overestimation of the bounds. As this point if S=K_1 e+e ̇+K_2 ∑▒〖e=0〗 is chosen as desired sliding surface, if the dynamic of spherical motor is derived to sliding surface and if switching function is used to reduce the challenge of uncertainty then the linearization and decoupling through the use of feedback, not gears, can be realized. Because, when the system dynamic is on the sliding surface and switching function is used the derivative of sliding surface S ̇=K_1 e ̇+e ̈+K_2 e is equal to the zero that is a decoupled and linearized closed-loop spherical motor dynamics that one expects in computed torque control. Linearization and decoupling by the above method can be obtained in spite of the quality of the spherical motor dynamic model, in contrast to the computed-torque control that requires the exact dynamic model of a system.
It is well known fact that if the uncertainties are very good compensate there is no need to use discontinuous part which create the high frequency chattering. To compensate the uncertainties fuzzy logic theory is a good candidate, but design a fuzzy controller with perfect dynamic compensation in presence of uncertainty is very difficult. Therefore, if the uncertainties are estimated and if the estimation results are used by discontinuous feedback control, and if low pass filter is added to this part, high frequency oscillation can be eliminated.
Finally, for a linear and partially decoupled dynamics of the robot manipulator, when the result is near to the sliding surface, a linear controller is designed based on the deviation of state trajectories from the sliding surface. The above discussion gives rational for selecting the proposed methodology in this research.
Iranian Institute of Advance Science and Technology
A Field Programmable Gate Array (FPGAs) is a small Field Programmable Device (FPD) that supports ... more A Field Programmable Gate Array (FPGAs) is a small Field Programmable Device (FPD) that supports thousands of logic gates. FPGA is a high speed, low cost, short time to market and small device size. Technically speaking an FPGA can be used to solve any problem which is computable. This is trivially proven by the fact FPGA can be used to implement a Soft microprocessor. Their advantage lies in that they are sometimes significantly faster for some applications due to their parallel nature and optimality in terms of the number of gates used for a certain process. Specific applications of FPGAs include digital signal processing, software-defined radio, ASIC prototyping, medical imaging, computer vision, speech recognition, nonlinear control, cryptography, bioinformatics, computer hardware emulation, radio astronomy, metal detection and a growing range of other areas.
Traditionally, FPGAs have been reserved for specific vertical applications where the volume of production is small. For these low-volume applications, the premium that companies pay in hardware costs per unit for a programmable chip is more affordable than the development resources spent on creating an ASIC for a low-volume application. Today, new cost and performance dynamics have broadened the range of viable applications. An FPGA chip is programmed by Hardware Description Language (HDL) which contains two types of languages, Very High Description Language (VHDL) and Verilog. VHDL is one of the powerful programming languages that can be used to describe the hardware design. VHDL was developed by the Institute of Electrical and Electronics Engineers (IEEE) in 1987 and Verilog was developed by Gateway Design Automation in 1984. In any application that requires real time processing, such as real time control applications, parallel Xilinx implementations are needed to speed up the hardware. This research focuses on FPGA-based nonlinear technique control for nonlinear system. FPGAs Xilinx Spartan 3E families are one of the most powerful flexible Hardware Language Description (HDL) programmable IC’s. To have the high speed processing FPGA based nonlinear controller in Xilinx ISE 9.1 is designed and implemented. In this project the conventional or intelligent nonlinear controller is implemented in FPGA to modify the result of industrial robot, IC engine, continuum robot and spherical motor.
Designing an effective procedure for fault detection and identification (FDI) is necessary to mai... more Designing an effective procedure for fault detection and identification (FDI) is necessary to maintain the healthy and safe operation of robot manipulators. The complexities of nonlinear parameters inherent in a robot manipulator make it challenging to detect and identify faults. To address this issue, a powerful, robust, hybrid fault identification method based on the fuzzy extended ARX-Laguerre proportional integral (PI) observer for perturbation robot manipulators is presented. Accurate fault estimation is an essential challenge in classical extended ARX-Laguerre PI observers. The Takagi-Sugeno (T-S) fuzzy algorithm is applied to the sliding mode extended ARX-Laguerre PI observer to modify the performance of fault estimation. Moreover, using the ARX-Laguerre algorithm, PI observation technique, sliding mode estimation method, and T-S fuzzy procedure, the system's performance showed fast convergence and high accuracy. A PUMA robot manipulator was used to test the effectiveness of the proposed method. Results indicated that the proposed algorithm outperforms the ARX-Laguerre PI observer performance.
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Papers by FARZIN PILTAN
The proper design of controllers for various kinds of systems involving unknown conditions and highly nonlinear and uncertain dynamics remains an open research topic. Meanwhile, machine-learning-based algorithms have been used in several fields, especially when massive amounts of data and great computing power are needed. Research in the field of machine learning aiming to solve issues of flexibility and complexity is ongoing. The connection between (modern) control theory and machine learning is very important in view of surpassing the potentialities of each discipline.
On this note, "control and learning" techniques are presently used in the driving technology underpinning a whole new generation of autonomous devices and cognitive artifacts that, through their learning capabilities, interact seamlessly with the world around them, hence providing the missing link between the digital and physical worlds.
Moreover, control and learning techniques are often used in various industries for control, fault detection, fault diagnosis, and fault-tolerant control. To address these issues, there is a need to develop hybrid algorithms based on control and/or learning; such algorithms can be recommended in this Special Issue.
This Special Issue will focus on control, modeling, various machine learning techniques, fault diagnosis, and fault-tolerant control for systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of various systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to:
Modeling and identification
Adaptive and hybrid control
Adaptive and hybrid observers
Reinforcement learning for control
Data-driven control
Fault diagnosis
Fault-tolerant control of systems based on various control and learning techniques
This Special Issue focuses on mechanics, control, modeling, fault diagnosis, and fault-tolerant control for robotic systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of robotic systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to, the following: modeling, control, fault diagnosis, and fault-tolerant control of robotic systems based on various techniques such as model-based techniques (e.g., sliding mode technique, feedback linearization algorithm, backstepping technique, Lyapunov-based method, etc.), knowledge-based algorithm (e.g., deep learning, transfer learning, fuzzy algorithm, neural network methods, and neuro-fuzzy inference techniques), hybrid methods (e.g., intelligent sliding mode technique, intelligent feedback linearization method, and intelligent backstepping algorithm), and adaptive techniques.
Prof. Dr. Jong-Myon Kim
Dr. Farzin Piltan
Guest Editors
https://www.mdpi.com/journal/applsci/special_issues/_Robots
mathematical models of a dynamic system based on
measured data. To design the best controllers for linear or
nonlinear systems, mathematical modeling is the main
challenge. To solve this challenge conventional and
intelligent identification are recommended. System
identification is divided into different algorithms. In this
research, two important types algorithm are compared to
identifying the highly nonlinear systems, namely: Auto-
Regressive with eXternal model input (ARX) and Auto
Regressive moving Average with eXternal model input
(Armax) Theory. These two methods are applied to the
highly nonlinear industrial motor.
The proper design of controllers for various kinds of systems involving unknown conditions and highly nonlinear and uncertain dynamics remains an open research topic. Meanwhile, machine-learning-based algorithms have been used in several fields, especially when massive amounts of data and great computing power are needed. Research in the field of machine learning aiming to solve issues of flexibility and complexity is ongoing. The connection between (modern) control theory and machine learning is very important in view of surpassing the potentialities of each discipline.
On this note, "control and learning" techniques are presently used in the driving technology underpinning a whole new generation of autonomous devices and cognitive artifacts that, through their learning capabilities, interact seamlessly with the world around them, hence providing the missing link between the digital and physical worlds.
Moreover, control and learning techniques are often used in various industries for control, fault detection, fault diagnosis, and fault-tolerant control. To address these issues, there is a need to develop hybrid algorithms based on control and/or learning; such algorithms can be recommended in this Special Issue.
This Special Issue will focus on control, modeling, various machine learning techniques, fault diagnosis, and fault-tolerant control for systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of various systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to:
Modeling and identification
Adaptive and hybrid control
Adaptive and hybrid observers
Reinforcement learning for control
Data-driven control
Fault diagnosis
Fault-tolerant control of systems based on various control and learning techniques
This Special Issue focuses on mechanics, control, modeling, fault diagnosis, and fault-tolerant control for robotic systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of robotic systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to, the following: modeling, control, fault diagnosis, and fault-tolerant control of robotic systems based on various techniques such as model-based techniques (e.g., sliding mode technique, feedback linearization algorithm, backstepping technique, Lyapunov-based method, etc.), knowledge-based algorithm (e.g., deep learning, transfer learning, fuzzy algorithm, neural network methods, and neuro-fuzzy inference techniques), hybrid methods (e.g., intelligent sliding mode technique, intelligent feedback linearization method, and intelligent backstepping algorithm), and adaptive techniques.
Prof. Dr. Jong-Myon Kim
Dr. Farzin Piltan
Guest Editors
https://www.mdpi.com/journal/applsci/special_issues/_Robots
mathematical models of a dynamic system based on
measured data. To design the best controllers for linear or
nonlinear systems, mathematical modeling is the main
challenge. To solve this challenge conventional and
intelligent identification are recommended. System
identification is divided into different algorithms. In this
research, two important types algorithm are compared to
identifying the highly nonlinear systems, namely: Auto-
Regressive with eXternal model input (ARX) and Auto
Regressive moving Average with eXternal model input
(Armax) Theory. These two methods are applied to the
highly nonlinear industrial motor.
Contact US: piltan_f@iranssp.com
Price: 1500 USD
Time: 200 hours
1) intro to MATLAB/SIMULINK
2) intro to 3-D motor and extract the system dynamics and kinematics from impact journal papers
3)implement dynamic and kinematics of 3-D motor using MATLAB/SIMULINK.
4)intro to linear and nonlinear controllers, compare between all types of controller
5) Design linear, conventional nonlinear and fuzzy controller for 3-D motor and compare them to select the best controller.
6)modify the select control method based on intelligence method and conventional control methodology
7)test and result discussion
8) extract journal papers and send to indexed journal"
1)To modelling and implementation of robot manipulator based on paper using Matlab/simulink
2) Design a linear and nonlinear controller
3)Test and result
the most important challenging works. This course focuses on the design, implementation and
analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot
manipulator and compare to computed torque controller, in presence of uncertainties. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.)
research center, Shiraz, Iran.
2
performance) but they have slight chattering in the presence of uncertainties. Sliding mode controller and application in FPGA has many advantages such as high speed, low cost, short time to market and small device size. FPGA can be used to design a controller in a single chip Integrated Circuit (IC). In this research the SMC is designed using VHDL language for implementation on FPGA device (XA3S1600E-Spartan-3E), with minimum chattering and high processing speed (63.29 MHz).
1. Chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers
2. Dependence of the nonlinear dynamic formulation
To solve the first challenge linear function was introduced, because this method could reduce the high frequency oscillation. To solve the second challenge in this project intelligent methodology is introduced.
Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, multi inputs-multi outputs and time variant. One purpose of accurate modeling is to save development costs of real engines and minimizing the risks of damaging an engine when validating controller designs. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity.
An intelligent nonlinear estimator with varying parameter gain is designed with guaranteed stability to allow implementation of the proposed state nonlinear methodology, where the nonlinear control strategy is implemented into a model engine control module. To estimate the dynamic model of IC engine fuzzy inference engine is applied to pure nonlinear control. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired fuel-air ratio and fuel mass over a given time interval.
Robotic surgery, computer-assisted surgery, and robotically-assisted surgery are terms for technological developments that use robotic systems to aid in surgical procedures. Robotically-assisted surgery was developed to overcome the limitations of minimally-invasive surgery and to enhance the capabilities of surgeons performing open surgery.
Major advances aided by surgical robots have been remote surgery, minimally invasive surgery and unmanned surgery. Due to robotic use, the surgery is done with precision, miniaturization, smaller incisions; decreased blood loss, less pain, and quicker healing time. Articulation beyond normal manipulation and three-dimensional magnification helps resulting in improved ergonomics. Due to these techniques there is a reduced duration of hospital stays, blood loss, transfusions, and use of pain medication. The existing open surgery technique has many flaws like limited access to surgical area, long recovery time, long hours of operation, blood loss, surgical scars and marks.
Compared with other minimally invasive surgery approaches, robot-assisted surgery gives the surgeon better control over the surgical instruments and a better view of the surgical site. In addition, surgeons no longer have to stand throughout the surgery and do not tire as quickly. Naturally, the surgical robot can continuously be used by rotating surgery teams. Finally, occurring hand tremors are filtered out by the robot’s controllers.
This research contributes to the filtering the hand tremors in continuum robot manipulator based on new technical methodology. Intelligent-Backstepping methodology is selected to reduce the hand tremors in continuum robot. This research addresses two basic issues related to the filtering hand tremors in continuum robots; (1) a more accurate representation of the dynamic model of an existing prototype, and (2) the design of a robust nonlinear filter. The nonlinear filter developed in this research is designed into two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation. Next, the artificial intelligence methodologies applied to it to solution uncertainty problem and reduce the hand tremors. Two factors are important in this research: system flexibility and time of response. To improve these two factors new methodology of intelligent-backstepping method is introduced.
For high precession trajectory planning and control, it is necessary to replace the actuator system made up of several single-DOF motors connected in series and/or parallel with a single multi-DOF actuator. The need for such systems has motivated years of research in the development of unusual, yet high performance actuators that have the potential to realize multi-DOF motion in a single joint. One such actuator is the spherical motor.
Compared to conventional robotic manipulators that offer the same motion capabilities, the spherical motor possesses several advantages. Not only can the motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace. The spherical motor is much simpler and more compact in design than most multiple single-axis robotic manipulators. The motor is also relatively easy to manufacture. The spherical motor have potential contributions to a wide range of applications such as coordinate measuring, object tracking, material handling, automated assembling, welding, and laser cutting. All these applications require high precision motion and fast dynamic response, which the spherical motor is capable of delivering. Previous research efforts on the spherical motor have demonstrated most of these features. These, however, come with a number of challenges. The spherical motor exhibits coupled, nonlinear and very complex dynamics. The design and implementation of feedback controllers for the motor are complicated by these dynamics. The controller design is further complicated by the orientation-varying torque generated by the spherical motor.
An Important question which comes to mind is that why this proposed methodology should be used when lots of control techniques are accessible to design high precision motion and fast dynamic response?
Answering to this question is the main objective in this part.
The dynamics of a spherical motor is highly nonlinear, time variant, MIMO, uncertain and there exist strong coupling effects between joints.
Spherical motor’s dynamic models through a large number of highly nonlinear parameters generate the problem of computation as a result it is caused to many challenges for real¬ time applications. To eliminate the actual acceleration measurement and also the computation burden as well as have stabile, efficiency and robust controller, base-line sliding mode controller is introduced. Assuming unstructured uncertainties and structure uncertainties can be defined into one term and considered as an uncertainty and external disturbance, the problem of computation burden and large number of parameters can be solved to some extent. Hence conventional switching sliding mode controller is an apparent nominates to design a controller using the bounds of the uncertainties and external disturbance. There are three main issues limiting the applications of conventional sliding mode controller; dynamic-based formulation of conventional control method, computation of the bounds of uncertainties, and high frequency oscillation. The problem in time of system response dynamic formulation of spherical motor is not a simple task and minimum rule-base fuzzy logic theory is used to reduce this challenge. If PID fuzzy logic controller is used, we have limitation in the number of fuzzy rule table. Consequently in this design PID fuzzy is extract by PD fuzzy plus PI fuzzy theory. To have a good design, PID fuzzy logic controller will be work based on PD fuzzy rule table in three parts of fuzzy logic area.
Uncertainties are very important challenges and caused to overestimation of the bounds. As this point if S=K_1 e+e ̇+K_2 ∑▒〖e=0〗 is chosen as desired sliding surface, if the dynamic of spherical motor is derived to sliding surface and if switching function is used to reduce the challenge of uncertainty then the linearization and decoupling through the use of feedback, not gears, can be realized. Because, when the system dynamic is on the sliding surface and switching function is used the derivative of sliding surface S ̇=K_1 e ̇+e ̈+K_2 e is equal to the zero that is a decoupled and linearized closed-loop spherical motor dynamics that one expects in computed torque control. Linearization and decoupling by the above method can be obtained in spite of the quality of the spherical motor dynamic model, in contrast to the computed-torque control that requires the exact dynamic model of a system.
It is well known fact that if the uncertainties are very good compensate there is no need to use discontinuous part which create the high frequency chattering. To compensate the uncertainties fuzzy logic theory is a good candidate, but design a fuzzy controller with perfect dynamic compensation in presence of uncertainty is very difficult. Therefore, if the uncertainties are estimated and if the estimation results are used by discontinuous feedback control, and if low pass filter is added to this part, high frequency oscillation can be eliminated.
Finally, for a linear and partially decoupled dynamics of the robot manipulator, when the result is near to the sliding surface, a linear controller is designed based on the deviation of state trajectories from the sliding surface. The above discussion gives rational for selecting the proposed methodology in this research.
Traditionally, FPGAs have been reserved for specific vertical applications where the volume of production is small. For these low-volume applications, the premium that companies pay in hardware costs per unit for a programmable chip is more affordable than the development resources spent on creating an ASIC for a low-volume application. Today, new cost and performance dynamics have broadened the range of viable applications. An FPGA chip is programmed by Hardware Description Language (HDL) which contains two types of languages, Very High Description Language (VHDL) and Verilog. VHDL is one of the powerful programming languages that can be used to describe the hardware design. VHDL was developed by the Institute of Electrical and Electronics Engineers (IEEE) in 1987 and Verilog was developed by Gateway Design Automation in 1984. In any application that requires real time processing, such as real time control applications, parallel Xilinx implementations are needed to speed up the hardware. This research focuses on FPGA-based nonlinear technique control for nonlinear system. FPGAs Xilinx Spartan 3E families are one of the most powerful flexible Hardware Language Description (HDL) programmable IC’s. To have the high speed processing FPGA based nonlinear controller in Xilinx ISE 9.1 is designed and implemented. In this project the conventional or intelligent nonlinear controller is implemented in FPGA to modify the result of industrial robot, IC engine, continuum robot and spherical motor.