Sliding-Mode Control
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Recent papers in Sliding-Mode Control
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., Internal Combustion Engine) is one of the most important challenging works. This paper focuses on the design of a robust backstepping adaptive... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., Internal Combustion Engine) is one of the most important challenging works. This paper focuses on the design of a robust backstepping adaptive feedback linearization controller (FLC) for internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, feedback linearization controller is selected. Pure feedback linearization controller can be used to control of partly unknown nonlinear dynamic parameters of IC engine. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is applied to pure feedback linearization controller. The results demonstrate that the error-based fuzzy feedback linearization controller is a model-free controllers which works well in certain and partly uncertain system. Pure feedback linearization controller and error-based feedback linearization like controller with have difficulty in handling unstructured model uncertainties. To solve this problem applied backstepping-based tuning method to error-based fuzzy feedback linearization controller for adjusting the feedback linearization controller gain ( ). This controller has acceptable performance in presence of uncertainty (e.g., overshoot=1%, rise time=0.48 second, steady state error = 1.3e-9 and RMS error=1.8e-11).
Refer to this research, a linear error-based tuning sliding mode controller (LTSMC) is proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller in a partly uncertain dynamic system’s parameters.... more
Refer to this research, a linear error-based tuning sliding mode controller (LTSMC) is proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller in a partly uncertain dynamic system’s parameters. Sliding mode controller has difficulty in handling unstructured model uncertainties. It is possible to solve this problem by combining sliding mode controller and adaption law which this method can helps improve the system’s tracking performance by online tuning method. Since the sliding surface gain ( ) is adjusted by new linear tuning method, it is continuous. In this research new is obtained by the previous multiple sliding surface slopes updating factor which is a coefficient varies between half to one. Linear error-based tuning sliding mode controller is stable model-based controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in linear error-based tuning sliding mode controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state error = 1.8e-10 and RMS error=1.16e-12).
The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller with application to spherical motor is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to... more
The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller with application to spherical motor is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 343 rules. It is too much work to write 343 rules and have lots of problem to design embedded control system e.g., Field Programmable Gate Array (FPGA). In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a conventional PI controller to have the minimum rule base and acceptable trajectory follow disturbance to control of spherical motor. However computed torque controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters for each direction of three degree of freedom spherical motor, this controller is work based on motor dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear spherical motor‟s dynamic equation. This research is used to reduce or eliminate the computed torque controller problem based on minimum rule base fuzzy logic theory to control of three degrees of freedom spherical motor system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
Index Term—PID
Index Term—PID
This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot... more
This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models is design and analyzes. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Simulation results for a planar application of the continuum or hyper-redundant robot manipulator (CRM) are provided to illustrate the performance of the developed adaptive controller. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In this research, a joint level controller for continuum robots is described which utilizes a fuzzy methodology component to compensate for dynamic uncertainties.
This research is focused on novel particle swarm optimization (PSO) SISO Lyapunov based fuzzy estimator sliding mode algorithms derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the... more
This research is focused on novel particle swarm optimization (PSO) SISO Lyapunov based fuzzy
estimator sliding mode algorithms derived in the Lyapunov sense. The stability of the closed-loop system is
proved mathematically based on the Lyapunov method. PSO SISO fuzzy compensate sliding mode
method design a SISO fuzzy system to compensate for the dynamic model uncertainties of the nonlinear
dynamic system and chattering also solved by nonlinear fuzzy saturation like method. Adjust the sliding
function is played important role to reduce the chattering phenomenon and also design acceptable
estimator applied to nonlinear classical controller so PSO method is used to off-line tuning. Classical
sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode
method with a switching control low guarantees the stability of the certain and/or uncertain system, but the
addition of the switching control low introduces chattering into the system. One way to reduce or eliminate
chattering is to insert a nonlinear (fuzzy) boundary like layer method inside of a boundary layer around the
sliding surface. Classical sliding mode control method has difficulty in handling unstructured model
uncertainties. One can overcome this problem by applied fuzzy inference system into sliding mode
algorithm to design and estimate model-free nonlinear dynamic equivalent part. To approximate a timevarying
nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large
number of fuzzy rules will cause a high computation load. The addition of PSO method to a fuzzy sliding
mode controller to tune the parameters of the fuzzy rules in use will ensure a moderate computational
load. The PSO method in this algorithm is designed based on the PSO stability theorem. Asymptotic
stability of the closed loop system is also proved in the sense of Lyapunov.
estimator sliding mode algorithms derived in the Lyapunov sense. The stability of the closed-loop system is
proved mathematically based on the Lyapunov method. PSO SISO fuzzy compensate sliding mode
method design a SISO fuzzy system to compensate for the dynamic model uncertainties of the nonlinear
dynamic system and chattering also solved by nonlinear fuzzy saturation like method. Adjust the sliding
function is played important role to reduce the chattering phenomenon and also design acceptable
estimator applied to nonlinear classical controller so PSO method is used to off-line tuning. Classical
sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode
method with a switching control low guarantees the stability of the certain and/or uncertain system, but the
addition of the switching control low introduces chattering into the system. One way to reduce or eliminate
chattering is to insert a nonlinear (fuzzy) boundary like layer method inside of a boundary layer around the
sliding surface. Classical sliding mode control method has difficulty in handling unstructured model
uncertainties. One can overcome this problem by applied fuzzy inference system into sliding mode
algorithm to design and estimate model-free nonlinear dynamic equivalent part. To approximate a timevarying
nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large
number of fuzzy rules will cause a high computation load. The addition of PSO method to a fuzzy sliding
mode controller to tune the parameters of the fuzzy rules in use will ensure a moderate computational
load. The PSO method in this algorithm is designed based on the PSO stability theorem. Asymptotic
stability of the closed loop system is also proved in the sense of Lyapunov.
This paper describes the MATLAB/SIMULINK realization of the PUMA 560 robot manipulator position control methodology. This paper focuses on design, analyzed and implements nonlinear computed torque control (CTC) methods. These simulation... more
This paper describes the MATLAB/SIMULINK realization of the PUMA 560 robot manipulator
position control methodology. This paper focuses on design, analyzed and implements nonlinear
computed torque control (CTC) methods. 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.
position control methodology. This paper focuses on design, analyzed and implements nonlinear
computed torque control (CTC) methods. 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.
The internship will have as main objective the modeling of Quadrotor using the environment Matlab / Simulink . to do this I will concentrate on the principle of flight, then I will attack the control and the stabilization of the drone... more
The internship will have as main objective the modeling of Quadrotor using the environment Matlab / Simulink . to do this I will concentrate on the principle of flight, then I will attack the control and the stabilization of the drone by using different methods non-linear and robust control that will guarantee the stabilization and good control of the flying drone.
The first part is devoted to the presentation of the adopted model. Then we will demonstrate the algorithms for calculating the set of commands used, all we will make a presentation and analysis of the results of simulations for the purpose of making a comparison between the algorithms of controllers developed in terms of dynamic performance and their ability to stabilize. the system during flight using different trajectories (simple and complex) variations in time and under the effect of possible disturbance. Finally we will detail the equations of a nonlinear observer in sliding mode for the estimation of states of our system.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Le stage aura comme objectif principal la modélisation d’un drone du type Quadrotor sous l’environnement Matlab/Simulink pour ce faire je vais me concentrer sur le principe de vol, puis je vais attaquer la commande et la stabilisation du drone en utilisant différents types de commande non linéaire et robuste qui va nous garantir la stabilisation et la bonne commande de l’engin volant.
La première partie est consacrée à la présentation du modèle adopté. Ensuite, on démontrera les algorithmes de calcul de l’ensemble des commandes utilisées, toutes on fera une présentation et analyse des résultats des simulations pour le but de faire une comparaison entre les algorithmes des contrôleurs développés en termes de performances dynamiques et leurs capacités à stabiliser le système pendant le vol sous différentes trajectoires (simples et complexes) variantes dans le temps et sous l’effet de perturbation possible. Finalement on détaillera les équations d’un observateur non linéaire en régime glissant pour l’estimation
d’états de notre système.
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Si vous avez des questions sur ce travail, n'hésitez pas à me contacter sur mon adresse mail.
If you have any questions about this project, do not hesitate to contact me on my email address.
e-mail : iliass.elmouderrib@gmail.com
good reading!
The first part is devoted to the presentation of the adopted model. Then we will demonstrate the algorithms for calculating the set of commands used, all we will make a presentation and analysis of the results of simulations for the purpose of making a comparison between the algorithms of controllers developed in terms of dynamic performance and their ability to stabilize. the system during flight using different trajectories (simple and complex) variations in time and under the effect of possible disturbance. Finally we will detail the equations of a nonlinear observer in sliding mode for the estimation of states of our system.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Le stage aura comme objectif principal la modélisation d’un drone du type Quadrotor sous l’environnement Matlab/Simulink pour ce faire je vais me concentrer sur le principe de vol, puis je vais attaquer la commande et la stabilisation du drone en utilisant différents types de commande non linéaire et robuste qui va nous garantir la stabilisation et la bonne commande de l’engin volant.
La première partie est consacrée à la présentation du modèle adopté. Ensuite, on démontrera les algorithmes de calcul de l’ensemble des commandes utilisées, toutes on fera une présentation et analyse des résultats des simulations pour le but de faire une comparaison entre les algorithmes des contrôleurs développés en termes de performances dynamiques et leurs capacités à stabiliser le système pendant le vol sous différentes trajectoires (simples et complexes) variantes dans le temps et sous l’effet de perturbation possible. Finalement on détaillera les équations d’un observateur non linéaire en régime glissant pour l’estimation
d’états de notre système.
-----------------------------------------------------------------------------------------------------------------------------------
Si vous avez des questions sur ce travail, n'hésitez pas à me contacter sur mon adresse mail.
If you have any questions about this project, do not hesitate to contact me on my email address.
e-mail : iliass.elmouderrib@gmail.com
good reading!
In this study, a mathematical tunable gain model free PID-like sliding mode fuzzy controller (GTSMFC) is designed to rich the best performance. Sliding mode fuzzy controller is studied because of its model free, stable and high... more
In this study, a mathematical tunable gain model free PID-like sliding mode fuzzy controller (GTSMFC) is designed to rich the best performance. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. Today, most of systems (e.g., robot manipulators) are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools (e.g., nonlinear sliding mode controller) are used in artificial intelligent control methodologies to design model free nonlinear robust controller with high performance (e.g., minimum error, good trajectory, disturbance rejection). Non linear classical theories have been applied successfully in many applications, but they also have some limitation. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on applied sliding mode controller in fuzzy logic theory to solve the limitation in fuzzy logic controller and sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the gain updating factor and sliding surface slope in PID like sliding mode fuzzy controller to have the best performance and reduce the limitation.
Refer to this paper, an intelligent-fuzzy feed-forward computed torque estimator for Proportional-Integral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID... more
Refer to this paper, an intelligent-fuzzy feed-forward computed torque estimator for Proportional-Integral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately and it is the most used in robot manipulators. In order to remove steady-state error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. Model-based compensation for PD control is an alternative method to substitute PID control. Computed torque compensation is one of the nonlinear compensator. The main problem of the pure computed torque compensator (CTC) was highly nonlinear dynamic parameters which related to system’s dynamic parameters in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using feed-forward fuzzy inference system. To eliminate the continuum robot manipulator system’s dynamic; Mamdani fuzzy inference system is design and applied to CTC. This methodology is based on design feed-forward fuzzy inference system and applied to CTC. The results demonstrate that the model base feed-forward fuzzy CTC estimator works well to compensate linear PID controller in presence of partly uncertainty system (e.g., continuum robot).
In this paper, a PD-serial fuzzy based robust nonlinear estimator for a robot manipulator is proposed by using robust factorization approach. That is, considering the uncertainties of dynamic model consisting of measurement error and... more
In this paper, a PD-serial fuzzy based robust nonlinear estimator for a robot manipulator is proposed by using robust factorization approach. That is, considering the uncertainties of dynamic model consisting of measurement error and disturbances, a PD with fuzzy estimator variable structure nonlinear feedback control scheme is designed to reduce effect of uncertainties. This research aims to design a new methodology to fix the position in robot manipulator. PD method is a linear methodology which can be used for highly nonlinear system’s (e.g., robot manipulator). To estimate this method, new serial fuzzy variable structure method (PD.FVSM) is used. This estimator can estimate the parameters to have the best performance.
Both fuzzy logic and sliding mode can compensate the steady-state error of proportional-derivative (PD) control. This paper presents parallel sliding mode compensations for fuzzy PD controllers. The asymptotic stability of fuzzy PD... more
Both fuzzy logic and sliding mode can compensate the steady-state error of proportional-derivative (PD) control. This paper presents parallel sliding mode compensations for fuzzy PD controllers. The asymptotic stability of fuzzy PD control with first-order sliding mode compensation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design, implementation and analysis of a chattering free sliding mode... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of
the most important challenging works. This paper 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. In order to
provide high performance nonlinear methodology, sliding mode controller and computed torque
controller are selected. Pure sliding mode controller and computed torque 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 an important drawback namely;
chattering phenomenon which it can causes some problems such as saturation and heat the
mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research
is used the linear saturation function boundary layer method instead of switching function method
in pure sliding mode controller. 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.
the most important challenging works. This paper 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. In order to
provide high performance nonlinear methodology, sliding mode controller and computed torque
controller are selected. Pure sliding mode controller and computed torque 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 an important drawback namely;
chattering phenomenon which it can causes some problems such as saturation and heat the
mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research
is used the linear saturation function boundary layer method instead of switching function method
in pure sliding mode controller. 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.
This paper expands a sliding mode fuzzy controller which sliding surface gain is optimized by Gradient Descent Optimization Algorithm (GDOA). The main goal is to guarantee acceptable trajectories tracking between the second order... more
This paper expands a sliding mode fuzzy controller which sliding surface gain is optimized by Gradient Descent Optimization Algorithm (GDOA). The main goal is to guarantee acceptable trajectories tracking between the second order nonlinear system (robot manipulator) actual and the desired trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The outputs represent torque, respectively. The GDOA is the most prominent iterative method for solving sparse systems of nonlinear equations. The GDOA is a composite of simple, elegant ideas that almost anyone can understand. Pure sliding mode fuzzy controller has difficulty in handling unstructured model uncertainties. To solve this problem applied GDOA to sliding mode fuzzy controller for adjusting the sliding surface gain ( ). Since the sliding surface gain ( ) is adjusted by GDOA, it is nonlinear and continuous. GDOA sliding mode fuzzy controller is stable model-free controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in GDOA sliding mode fuzzy controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0.1%, rise time=0.6 second, steady state error = 1.1e-9 and RMS error=1.8e-9).
Refer to this paper, design lookup table changed adaptive fuzzy sliding mode controller with minimum rule base and good response in presence of structure and unstructured uncertainty is presented. However sliding mode controller is one... more
Refer to this paper, design lookup table changed adaptive fuzzy sliding mode controller with
minimum rule base and good response in presence of structure and unstructured uncertainty is
presented. However sliding mode controller is one of the robust nonlinear controllers but when
this controller is applied to robot manipulator with highly nonlinear and uncertain dynamic function;
caused to be challenged in control. Sliding mode controller in presence of uncertainty has two
most important drawbacks; chattering and nonlinear equivalent part which proposed method is
solved these challenges with look up table change methodology. This method is based on self
tuning methodology therefore artificial intelligence (e.g., fuzzy logic method) is played important
role to design proposed method. This controller has acceptable performance in presence of
uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS
error=0.00017).
minimum rule base and good response in presence of structure and unstructured uncertainty is
presented. However sliding mode controller is one of the robust nonlinear controllers but when
this controller is applied to robot manipulator with highly nonlinear and uncertain dynamic function;
caused to be challenged in control. Sliding mode controller in presence of uncertainty has two
most important drawbacks; chattering and nonlinear equivalent part which proposed method is
solved these challenges with look up table change methodology. This method is based on self
tuning methodology therefore artificial intelligence (e.g., fuzzy logic method) is played important
role to design proposed method. This controller has acceptable performance in presence of
uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS
error=0.00017).
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a robust rule base fuzzy-based tuning fuzzy sliding mode controller... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a robust rule base fuzzy-based tuning fuzzy sliding mode controller for second order nonlinear system in presence of uncertainties. Rule base is one of the important key factors in design of fuzzy based tuning controller. In order to provide high performance nonlinear methodology, fuzzy sliding mode controller is selected. Fuzzy sliding mode controller can be used to control of partly unknown nonlinear dynamic parameters of nonlinear system. Error-based fuzzy sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied improve rule base fuzzy-based tuning method to error-based fuzzy sliding mode controller for adjusting the rule base in adaptive fuzzy-based tuning. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state error = 1.3e-11 and RMS error=1.2e-11).
Refer to the research, review of sliding mode controller is introduced and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the... more
Refer to the research, review of sliding mode controller is introduced and application to robot 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, fuzzy logic controller and adaptive method, the
output in most of research have improved. Each method by adding to the previous algorithm has covered
negative points. Obviously robot manipulator is nonlinear, and a number of parameters are uncertain, this
research focuses on comparison between sliding mode algorithm which analyzed by many researcher.
Sliding mode controller (SMC) is one of the nonlinear robust controllers which it can be used in uncertainty
nonlinear dynamic systems. This nonlinear controller has two challenges namely nonlinear dynamic
equivalent part and chattering phenomenon. A review of sliding mode controller for robot manipulator will
be investigated in this research
has proposed in order to design high performance nonlinear controller in the presence of uncertainties.
Regarding to the positive points in sliding mode controller, fuzzy logic controller and adaptive method, the
output in most of research have improved. Each method by adding to the previous algorithm has covered
negative points. Obviously robot manipulator is nonlinear, and a number of parameters are uncertain, this
research focuses on comparison between sliding mode algorithm which analyzed by many researcher.
Sliding mode controller (SMC) is one of the nonlinear robust controllers which it can be used in uncertainty
nonlinear dynamic systems. This nonlinear controller has two challenges namely nonlinear dynamic
equivalent part and chattering phenomenon. A review of sliding mode controller for robot manipulator will
be investigated in this research
This research focuses on design Single Input Single Output (SISO) adaptive sliding mode fuzzy PD fuzzy sliding mode algorithm with estimates the equivalent part derived in the Lyapunov sense. The stability of the closed-loop system is... more
This research focuses on design Single Input Single Output (SISO) adaptive sliding mode fuzzy PD fuzzy sliding mode algorithm with estimates the equivalent part derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Proposed method introduces a SISO fuzzy system to compensate for the model uncertainties of the system and eliminate the chattering by linear boundary layer method. This algorithm is used a SISO fuzzy system to alleviate chattering and to estimate the control gain in the control law and presented a scheme to online tune of sliding function. To attenuate the chattering phenomenon this method developed a linear boundary layer and the parameter of the sliding function is online tuned by adaptation laws. This algorithm will be analyzed and evaluated on robotic manipulators and design adaption laws of adaptive algorithms after that writing Lyapunov function candidates and prove the asymptotic convergence of the closed-loop system using Lyapunov stability theorem mathematically. Compare and evaluate proposed method and sliding mode algorithms under disturbance. In regards to the former, we will be looking at the availability of online tuning methodology and the number of fuzzy if-then rules inherent to the fuzzy system being used and the corresponding computational load. Our analysis of the results will be limited to tracking accuracy and chattering.
In this study, a battery powered four-wheel drive electric vehicle with fuzzy logic sliding mode controller (FLSMC) is realized. The conventional sliding surface is modified using fuzzy rules sets. The dynamic sliding surfaces are... more
In this study, a battery powered four-wheel drive electric vehicle with fuzzy logic sliding mode controller (FLSMC) is realized. The conventional sliding surface is modified using fuzzy rules sets. The dynamic sliding surfaces are obtained by changing their sloped using the global error and its derivative in fuzzy logic inference system. The characteristics of the designed controller to
nonlinear J, B and load torque for EV Electric Drives System are observed. The proposed system control mechanism is digitally simulated by using the MATLAB/Simulink/SimPowerSystems software. Simulation results show that the system with FLSMC has good efficiency and performance.
nonlinear J, B and load torque for EV Electric Drives System are observed. The proposed system control mechanism is digitally simulated by using the MATLAB/Simulink/SimPowerSystems software. Simulation results show that the system with FLSMC has good efficiency and performance.
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical error-based tuning sliding mode... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of
the most important challenging works. This paper focuses on the design of a chattering free
mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic
robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear
methodology, sliding mode controller is selected. 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 an important drawback namely;
chattering phenomenon which it can causes some problems such as saturation and heat the
mechanical parts of robot manipulators or drivers.
In order to reduce the chattering this research is used the switching function in presence of
mathematical error-based method instead of switching function method in pure sliding mode
controller. The results demonstrate that the sliding mode controller with switching function is a
model-based controllers which works well in certain and partly uncertain system. Pure sliding
mode controller has difficulty in handling unstructured model uncertainties. To solve this problem
applied mathematical model-free tuning method to sliding mode controller for adjusting the sliding
surface gain (λ ). Since the sliding surface gain (λ) is adjusted by mathematical model free-based
tuning method, it is nonlinear and continuous. In this research new λ is obtained by the previous λ
multiple sliding surface slopes updating factor α. Chattering free mathematical error-based
tuning sliding mode controller is stable controller which eliminates the chattering phenomenon
without to use the boundary layer saturation function. Lyapunov stability is proved in
mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise
time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
the most important challenging works. This paper focuses on the design of a chattering free
mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic
robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear
methodology, sliding mode controller is selected. 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 an important drawback namely;
chattering phenomenon which it can causes some problems such as saturation and heat the
mechanical parts of robot manipulators or drivers.
In order to reduce the chattering this research is used the switching function in presence of
mathematical error-based method instead of switching function method in pure sliding mode
controller. The results demonstrate that the sliding mode controller with switching function is a
model-based controllers which works well in certain and partly uncertain system. Pure sliding
mode controller has difficulty in handling unstructured model uncertainties. To solve this problem
applied mathematical model-free tuning method to sliding mode controller for adjusting the sliding
surface gain (λ ). Since the sliding surface gain (λ) is adjusted by mathematical model free-based
tuning method, it is nonlinear and continuous. In this research new λ is obtained by the previous λ
multiple sliding surface slopes updating factor α. Chattering free mathematical error-based
tuning sliding mode controller is stable controller which eliminates the chattering phenomenon
without to use the boundary layer saturation function. Lyapunov stability is proved in
mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise
time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This research focuses on the design, implementation and analysis of a model-free linear error-based... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of
the most important challenging works. This research focuses on the design, implementation and
analysis of a model-free linear error-based tuning computed torque controller for highly nonlinear
dynamic second order system, in presence of uncertainties. In order to provide high performance
nonlinear methodology, computed torque controller is selected. Pure computed torque controller
can be used to control of partly known nonlinear dynamic parameters of nonlinear systems.
Conversely, pure computed torque controller is used in many applications; it has an important
drawback namely; nonlinear equivalent dynamic formulation in uncertain dynamic parameter. In
order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid
mathematical model base controller, model-free performance/error-based linear methodology
with three inputs and one output is applied to pure computed torque controller. The results
demonstrate that the error-based linear tuning computed torque controller is a model-based
controllers which works well in certain and uncertain system. Pure computed torque controller has
difficulty in handling unstructured model uncertainties. To solve this problem applied linear modelfree
error -based tuning method to computed torque controller for adjusting the linear inner loop
gain ( ). Since the linear inner loop gain () is adjusted by linear error-based tuning method, it is
linear and continuous. In this research new is obtained by the previous multiple gain updating
factor which is a coefficient varies between half to two.
the most important challenging works. This research focuses on the design, implementation and
analysis of a model-free linear error-based tuning computed torque controller for highly nonlinear
dynamic second order system, in presence of uncertainties. In order to provide high performance
nonlinear methodology, computed torque controller is selected. Pure computed torque controller
can be used to control of partly known nonlinear dynamic parameters of nonlinear systems.
Conversely, pure computed torque controller is used in many applications; it has an important
drawback namely; nonlinear equivalent dynamic formulation in uncertain dynamic parameter. In
order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid
mathematical model base controller, model-free performance/error-based linear methodology
with three inputs and one output is applied to pure computed torque controller. The results
demonstrate that the error-based linear tuning computed torque controller is a model-based
controllers which works well in certain and uncertain system. Pure computed torque controller has
difficulty in handling unstructured model uncertainties. To solve this problem applied linear modelfree
error -based tuning method to computed torque controller for adjusting the linear inner loop
gain ( ). Since the linear inner loop gain () is adjusted by linear error-based tuning method, it is
linear and continuous. In this research new is obtained by the previous multiple gain updating
factor which is a coefficient varies between half to two.
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical baseline sliding mode controller... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical baseline sliding mode controller (BSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties and external disturbance. In order to provide high performance nonlinear methodology, sliding mode controller and baseline methodology are selected. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers so baseline sliding mode controller is used to eliminate this important challenge. In order to reduce the chattering this research is used the switching function in presence of baseline method instead of switching function method in pure sliding mode controller. The results demonstrate that baseline sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system and have a better performance compare to pure sliding mode controller. Chattering free baseline sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function.
Dünya nüfusunun 6 milyarın üzerinde olması, son yıllardaki nüfustaki büyüme oranın %2.2 seviyelerine ulaşması çorbadan içecek olan kişi sayısını geçmiş yıllara göre daha da fazla artırması, küresel ısınma, iklim değişiklikleri, hava... more
Dünya nüfusunun 6 milyarın üzerinde olması, son yıllardaki nüfustaki büyüme oranın %2.2 seviyelerine ulaşması çorbadan içecek olan kişi sayısını geçmiş yıllara göre daha da fazla artırması, küresel ısınma, iklim değişiklikleri, hava kirliliği, su kirliliği ve toprak kirliliği gibi çevresel problemlere sebep olan ve çevre için uzun vadeli en büyük tehlikelerden biri durumuna gelen karbon dioksit (CO2) oranı ve dünyanın ortalama yüzey sıcaklığının 21. Yüzyılın sonuna kadar bir 2-3oC artacağı öngörüsü, yenilenebilir enerji kaynaklarının ulaşım araçlarında da kullanımı önemli bir seçenek haline gelmiştir. Petrol ürünlerinden bağımsız, çevre dostu elektrikli araçlar artık bir çok ülkede günlük ulaşım aracı olmaya başlamıştır. Elektrikli araçlarda kullanılan akülerin Rüzgar, Fotovoltaik ve Hidrojen Yakıt Hücreli yenilenebilir kaynaklardan beslenerek doldurulması ve/veya aracın doğrudan Fotovoltaik/yakıt hücresi gibi kaynaklarla sürülmesi dikkate alınan alternatifler olarak karşımıza çıkmaktadırlar. Bu çalışmada da doğrudan Fotovoltaik panellerden beslenen bir elektrikli aracın kontrol ve simülasyon çalışmaları yapılmaktadır. Geliştirilen modellerin simülasyonları Matlab/Simulink ortamında yapılarak sonuçlar ve ilgili tartışmalar yapılmaktadır.
In this research, an artificial chattering free adaptive fuzzy sliding mode control design and application to uncertain robotic manipulator has proposed in order to design high performance nonlinear controller in the presence of... more
In this research, an artificial chattering free adaptive fuzzy sliding mode control design and application to
uncertain 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, fuzzy logic controller
and adaptive method, the output has improved. Each method by adding to the previous controller has
covered negative points. The main target in this research is design of model free estimator on-line sliding
mode fuzzy algorithm for robot manipulator to reach an acceptable performance. Robot manipulators are
highly nonlinear, and a number of parameters are uncertain, therefore design model free controller using
both analytical and empirical paradigms are the main goal. Although classical sliding mode methodology
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. To solve the chattering fuzzy logic inference applied instead of dead zone
function. To solve the equivalent problems in classical sliding mode controller this paper focuses on
applied fuzzy logic method in classical controller. This algorithm works very well in certain environment but
in uncertain or various dynamic parameters, it has slight chattering phenomenon. The system performance
in sliding mode controller and fuzzy sliding mode controller are sensitive to the sliding function. Therefore,
compute the optimum value of sliding function for a system is the next challenge. This problem has solved
by adjusting sliding function of the adaptive method continuously 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 well as mathematical nonlinear equivalent part by applied fuzzy
supervisory method in sliding mode fuzzy controller and tuning the sliding function.
uncertain 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, fuzzy logic controller
and adaptive method, the output has improved. Each method by adding to the previous controller has
covered negative points. The main target in this research is design of model free estimator on-line sliding
mode fuzzy algorithm for robot manipulator to reach an acceptable performance. Robot manipulators are
highly nonlinear, and a number of parameters are uncertain, therefore design model free controller using
both analytical and empirical paradigms are the main goal. Although classical sliding mode methodology
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. To solve the chattering fuzzy logic inference applied instead of dead zone
function. To solve the equivalent problems in classical sliding mode controller this paper focuses on
applied fuzzy logic method in classical controller. This algorithm works very well in certain environment but
in uncertain or various dynamic parameters, it has slight chattering phenomenon. The system performance
in sliding mode controller and fuzzy sliding mode controller are sensitive to the sliding function. Therefore,
compute the optimum value of sliding function for a system is the next challenge. This problem has solved
by adjusting sliding function of the adaptive method continuously 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 well as mathematical nonlinear equivalent part by applied fuzzy
supervisory method in sliding mode fuzzy controller and tuning the sliding function.
Refer to this research, a baseline error-based tuning sliding mode controller (LTSMC) is proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller in a partly uncertain dynamic system’s... more
Refer to this research, a baseline error-based tuning sliding mode controller (LTSMC) is
proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller
in a partly uncertain dynamic system’s parameters. Sliding mode controller has difficulty in
handling unstructured model uncertainties. It is possible to solve this problem by combining
sliding mode controller and adaption law which this method can helps improve the system’s
tracking performance by online tuning method. Since the sliding surface gain (λ) is adjusted by
baseline tuning method, it is continuous. In this research new λ is obtained by the previous λ
multiple sliding surface slopes updating factor δ. Baseline error-based tuning sliding mode
controller is stable model-based controller which eliminates the chattering phenomenon without to
use the boundary layer saturation function. Lyapunov stability is proved in baseline error-based
tuning sliding mode controller based on switching (sign) function. This controller has acceptable
performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state
error = 1.8e-10 and RMS error=1.16e-12).
proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller
in a partly uncertain dynamic system’s parameters. Sliding mode controller has difficulty in
handling unstructured model uncertainties. It is possible to solve this problem by combining
sliding mode controller and adaption law which this method can helps improve the system’s
tracking performance by online tuning method. Since the sliding surface gain (λ) is adjusted by
baseline tuning method, it is continuous. In this research new λ is obtained by the previous λ
multiple sliding surface slopes updating factor δ. Baseline error-based tuning sliding mode
controller is stable model-based controller which eliminates the chattering phenomenon without to
use the boundary layer saturation function. Lyapunov stability is proved in baseline error-based
tuning sliding mode controller based on switching (sign) function. This controller has acceptable
performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state
error = 1.8e-10 and RMS error=1.16e-12).
Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators,... more
Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness. In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.
The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to their robust performance in a wide... more
The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy Computed Torque Controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI controller to have the minimum rule base. However computed torque controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator‟s dynamic equation. This research is used to reduce or eliminate the computed torque controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
One of the most active research areas in field of robotics is control of robot manipulator because this system has highly nonlinear dynamic parameters and most of dynamic parameters are unknown so design an acceptable controller is the... more
One of the most active research areas in field of robotics is control of robot manipulator because
this system has highly nonlinear dynamic parameters and most of dynamic parameters are
unknown so design an acceptable controller is the main goal in this work. To solve this challenge
position new estimation sliding mode fuzzy controller is introduced and applied to robot
manipulator. This controller can solve to most important challenge in classical sliding mode
controller in presence of highly uncertainty, namely; chattering phenomenon based on fuzzy
estimator and online tuning and equivalent nonlinear dynamic based on estimation. Proposed
method has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise
time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).
this system has highly nonlinear dynamic parameters and most of dynamic parameters are
unknown so design an acceptable controller is the main goal in this work. To solve this challenge
position new estimation sliding mode fuzzy controller is introduced and applied to robot
manipulator. This controller can solve to most important challenge in classical sliding mode
controller in presence of highly uncertainty, namely; chattering phenomenon based on fuzzy
estimator and online tuning and equivalent nonlinear dynamic based on estimation. Proposed
method has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise
time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).
This paper expands a sliding mode fuzzy controller which sliding surface gain is on-line tuned by minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories tracking between the second order nonlinear... more
This paper expands a sliding mode fuzzy controller which sliding surface gain is on-line tuned by
minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories
tracking between the second order nonlinear system (robot manipulator) actual and the desired
trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani’s
fuzzy inference system (FIS) and it has one input and one output. The input represents the
function between sliding function, error and the rate of error. The outputs represent torque,
respectively. The fuzzy inference system methodology is on-line tune the sliding surface gain
based on error-based fuzzy tuning methodology. Pure sliding mode fuzzy controller has difficulty
in handling unstructured model uncertainties. To solve this problem applied fuzzy-based tuning
method to sliding mode fuzzy controller for adjusting the sliding surface gain (λ ). Since the sliding
surface gain (λ) is adjusted by fuzzy-based tuning method, it is nonlinear and continuous. Fuzzybased
tuning sliding mode fuzzy controller is stable model-free controller which eliminates the
chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability
is proved in fuzzy-based tuning sliding mode fuzzy controller based on switching (sign) function.
minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories
tracking between the second order nonlinear system (robot manipulator) actual and the desired
trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani’s
fuzzy inference system (FIS) and it has one input and one output. The input represents the
function between sliding function, error and the rate of error. The outputs represent torque,
respectively. The fuzzy inference system methodology is on-line tune the sliding surface gain
based on error-based fuzzy tuning methodology. Pure sliding mode fuzzy controller has difficulty
in handling unstructured model uncertainties. To solve this problem applied fuzzy-based tuning
method to sliding mode fuzzy controller for adjusting the sliding surface gain (λ ). Since the sliding
surface gain (λ) is adjusted by fuzzy-based tuning method, it is nonlinear and continuous. Fuzzybased
tuning sliding mode fuzzy controller is stable model-free controller which eliminates the
chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability
is proved in fuzzy-based tuning sliding mode fuzzy controller based on switching (sign) function.
This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. VSC methodology is selected as a framework to... more
This paper examines single input single output (SISO) chattering free
variable structure control (VSC) which controller coefficient is on-line tuned
by fuzzy backstepping algorithm. VSC methodology is selected as a
framework to construct the control law and address the stability and
robustness of the close loop system based on Lyapunove formulation. The
main goal is to guarantee acceptable fuel ratio result and adjust. The
proposed approach effectively combines the design technique from variable
structure controller is based on Lyapunov and fuzzy estimator to estimate the
nonlinearity of undefined system dynamic in backstepping controller. The
input represents the function between variable structure function, error and
the rate of error. The outputs represent fuel ratio, respectively. The fuzzy
backstepping methodology is on-line tune the variable structure function
based on adaptive methodology. The performance of the SISO VSC which
controller coefficient is on-line tuned by fuzzy backstepping algorithm
(FBSAVSC) is validated through comparison with VSC and proposed
method. Simulation results signify good performance of trajectory in
presence of uncertainty torque load.
variable structure control (VSC) which controller coefficient is on-line tuned
by fuzzy backstepping algorithm. VSC methodology is selected as a
framework to construct the control law and address the stability and
robustness of the close loop system based on Lyapunove formulation. The
main goal is to guarantee acceptable fuel ratio result and adjust. The
proposed approach effectively combines the design technique from variable
structure controller is based on Lyapunov and fuzzy estimator to estimate the
nonlinearity of undefined system dynamic in backstepping controller. The
input represents the function between variable structure function, error and
the rate of error. The outputs represent fuel ratio, respectively. The fuzzy
backstepping methodology is on-line tune the variable structure function
based on adaptive methodology. The performance of the SISO VSC which
controller coefficient is on-line tuned by fuzzy backstepping algorithm
(FBSAVSC) is validated through comparison with VSC and proposed
method. Simulation results signify good performance of trajectory in
presence of uncertainty torque load.
The minimum rule base Proportional Integral Derivative (PID) Fuzzy backstepping Controller is presented in this research. The popularity of PID Fuzzy backstepping controller can be attributed to their robust performance in a wide range of... more
The minimum rule base Proportional Integral Derivative (PID) Fuzzy backstepping Controller is presented in this research. The popularity of PID Fuzzy backstepping controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy backstepping controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 7 × 7 × 7 = 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI-like controller to have the minimum rule base. However backstepping controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator‟s dynamic equation. This research is used to reduce or eliminate the backstepping controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
Refer to this research, a model free environmental management fuzzy sliding mode management (MF-FSMC) design and application to internal combustion (IC) engine has proposed in order to tuning the fuel ratio to reduce the pollution. Even... more
Refer to this research, a model free environmental management fuzzy sliding mode management (MF-FSMC) design and application to internal combustion (IC) engine has proposed in order to tuning the fuel ratio to reduce the pollution. Even though, sliding mode methodology (SMM) is used in wide range areas but it has some disadvantages; chattering phenomenon which caused to motor oscillation and instability in system and equivalent dynamic formulation due to unique safety environmental management controller design of the IC engine. According to the Institute for Market Oracle average fuel consumption of gasoline cars in Iran is about 11 liters per day while the average fuel consumption in other countries such as Germany and Japan is 2.5 in Britain, is 3.5 in France, is 1.9 in Canada is 6.5 and in America is 7.3 liters per day so this research focuses on design a new method to reduce the fuel ratio in a car to have a safe environment. The fuzzy function in fuzzy sliding mode methodology is based on Mamdani’s fuzzy inference system (MFIS) and it has one input and one output. The input represents the function between sliding theory, error and the rate of error. The outputs represent the dynamic estimator to estimate the nonlinear dynamic equivalent in fuzzy sliding mode algorithm. The fuzzy sliding mode methodology is tune the environment pollution based on tune the fuel ratio. Simulation results signify good performance of fuel ratio in presence of uncertainty and external disturbance.
The application of design nonlinear controller such as computed torque controller in control of 6 degrees of freedom (DOF) robot arm will be investigated in this research. One of the significant challenges in control algorithms is a... more
The application of design nonlinear controller such as computed torque controller in control of 6
degrees of freedom (DOF) robot arm will be investigated in this research. One of the significant
challenges in control algorithms is a linear behavior controller design for nonlinear systems (e.g.,
robot manipulator). Some of robot manipulators which work in industrial processes are controlled
by linear PID controllers, but the design of linear controller for robot manipulators is extremely
difficult because they are hardly nonlinear and uncertain. To reduce the above challenges, the
nonlinear robust controller is used to control of robot manipulator. Computed torque controller is a
powerful nonlinear controller under condition of partly uncertain dynamic parameters of system.
This controller is used to control of highly nonlinear systems especially for robot manipulators. To
adjust this controller’s coefficient baseline methodology is used and applied to CTC.
degrees of freedom (DOF) robot arm will be investigated in this research. One of the significant
challenges in control algorithms is a linear behavior controller design for nonlinear systems (e.g.,
robot manipulator). Some of robot manipulators which work in industrial processes are controlled
by linear PID controllers, but the design of linear controller for robot manipulators is extremely
difficult because they are hardly nonlinear and uncertain. To reduce the above challenges, the
nonlinear robust controller is used to control of robot manipulator. Computed torque controller is a
powerful nonlinear controller under condition of partly uncertain dynamic parameters of system.
This controller is used to control of highly nonlinear systems especially for robot manipulators. To
adjust this controller’s coefficient baseline methodology is used and applied to CTC.
This paper investigates the global chaos synchronization of identical Shimizhu-Morioka chaotic systems (Shimizu and Morioka, 1980) by sliding mode control. The stability results derived in this paper for the complete synchronization of... more
This paper investigates the global chaos synchronization of identical Shimizhu-Morioka chaotic systems (Shimizu and Morioka, 1980) by sliding mode control. The stability results derived in this paper for the complete synchronization of identical Shimizu-Morioka chaotic systems are established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the sliding mode control method is very effective and convenient to achieve global chaos synchronization of the identical Shimizu-Morioka chaotic systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper for the identical Shimizu-Morioka systems.
This research is focused on proposed minimum rule base PID computed torque algorithms with application to continuum robot manipulator. The stability of the closed-loop system is proved mathematically based on the Lyapunov method.... more
This research is focused on proposed minimum rule base PID computed torque algorithms with application to continuum robot manipulator. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Classical Computed Torque Controller (CTC) is robust to control model partly uncertainties and external disturbances. This controller is one of the significant nonlinear methodologies; according to the nonlinear dynamic formulation. One of the main targets in this research is increase the robustness based on the artificial intelligence methodology. Classical computed torque control has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a computed torque controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. To reduce the number of rule base this research is focused on the PD like fuzzy plus integral methodology. This method is applied to continuum robot manipulator to have the best performance.
This paper expands a Multi Input Multi Output (MIMO) fuzzy baseline variable structure control (VSC) which controller coefficient is off-line tuned by gradient descent algorithm. The main goal is to adjust the optimal value for fuel ratio... more
This paper expands a Multi Input Multi Output (MIMO) fuzzy baseline variable structure control (VSC) which controller coefficient is off-line tuned by gradient descent algorithm. The main goal is to adjust the optimal value for fuel ratio (FR) in motor engine. The fuzzy inference system in proposed methodology is works based on Mamdani-Lyapunov fuzzy inference system (FIS). To reduce dependence on the gain updating factor coefficients of the fuzzy methodology, PID baseline method is introduced. This new method provides an optimal setting for other factors which crated by PID baseline method. The gradient descent methodology is off-line tune all coefficients of baseline fuzzy and variable structure function based on mathematical optimization methodology. The performance of proposed methodology is validated through comparison with fuzzy variable structure methodology (FVSC). Simulation results signify good performance of fuel ratio in presence of different torque load and external disturbance.
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is the main challenge in this paper. This paper focuses on the design and analysis of a chattering free Mamdani’s fuzzy-based tuning gradient descent... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is the main challenge in this paper. This paper focuses on the design and analysis of a chattering free Mamdani’s fuzzy-based tuning gradient descent optimal error-based fuzzy sliding mode controller for highly nonlinear dynamic six degrees of freedom robot manipulator, in presence of uncertainties. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is applied to pure sliding mode controller. Pure sliding mode controller and error-based fuzzy sliding mode controller have difficulty in handling unstructured model uncertainties. To solve this problem applied fuzzy-based tuning method to error-based fuzzy sliding mode controller for adjusting the sliding surface gain. Since the sliding surface gain is adjusted by gradient descent optimization method. Fuzzy-based tuning gradient descent optimal error-based fuzzy sliding mode controller is stable model-free controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in fuzzy-based tuning gradient descent optimal fuzzy sliding mode controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
- by FARZIN PILTAN and +1
- •
- Electrical Engineering, Robotics, Mechatronics, Fuzzy Logic
A ballbot is a mobile robot with the ability to simultaneously move and balance on a spherical ball. In this study, we proposed control algorithms to stabilize the decoupled dynamics of a ballbot, which has three orthogonal planes. The... more
A ballbot is a mobile robot with the ability to simultaneously move and balance on a spherical ball. In this study, we proposed control algorithms to stabilize the decoupled dynamics of a ballbot, which has three orthogonal planes. The uncertainties are considered in the modeled dynamics. The operations of the whole system are separately controlled through stabilization on these planes. By using systematic backstepping techniques, a robust nonlinear controller based on a hierarchical sliding mode control is proposed to achieve balance and movement in the vertical planes. The closed-loop system stability is obtained by using the Lyapunov stability criterion and Barbalat's lemma. The robustness of the proposed algorithm in rejecting unknown lumped perturbations, which is a well-known characteristic of sliding mode control, is evaluated. A proportional-integral-derivative feedforward controller is proposed to control the heading in the horizontal plane. Several experiments were performed on a real system to validate the system design, and demonstrate the robustness of the proposed control algorithm for an underactuated mechanical system.
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive... more
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by new adaption method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One of the main targets in this research to reduce or eliminate chattering is to insert online tuning method. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov. This method is applied to continuum robot manipulator to have the best performance.
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