Ksouri Mekki
ESPRIT.TN, électromécanique, Faculty Member
- Ecole Nationale d'Ingénieurs de Tunis (ENIT), Génie Electrique, Faculty Memberadd
This paper investigates experimentally the application of several adaptive control algorithms for the control of a semi-batch chemical reactor. These algorithms combine parameter estimation algorithms and conventional control design... more
This paper investigates experimentally the application of several adaptive control algorithms for the control of a semi-batch chemical reactor. These algorithms combine parameter estimation algorithms and conventional control design methods to update the coefficients of the ...
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Research Interests: Engineering, Simulated Annealing, Product Design, Genetic Algorithm, Optimization Problem, and 12 moreMathematical Sciences, Case Study, Advanced manufacturing technology, Immune system, Design optimization, Hybrid Approach, Hybrid genetic algorithm, Pattern Search, Pressure Vessel, Particle Swarm, Particle Swarm Optimizer, and Job Shop Scheduling Problem
Abstract: This paper presents the identification and the control of a semi-batch reactor. The process is described by a parametric Volterra model. The control strategy uses a nonlinear model based predictive control law. A practical... more
Abstract: This paper presents the identification and the control of a semi-batch reactor. The process is described by a parametric Volterra model. The control strategy uses a nonlinear model based predictive control law. A practical procedure for the simultaneous estimation ...
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This work presents an application of linear and nonlinear robust predictive control onto a three tanks system. The design of the linear solution is based on Single-Input Single-Output Controlled Auto Regressive Integrated Moving Average... more
This work presents an application of linear and nonlinear robust predictive control onto a three tanks system. The design of the linear solution is based on Single-Input Single-Output Controlled Auto Regressive Integrated Moving Average (CARIMA) model and the nonlinear controller considers Nonlinear Auto Regressive with eXogenous output (NARX) model. Parametric uncertainties and polytopic uncertainties are adopted in order to take into account the uncertain behavior of the system. Based on worst case strategy, the control law is obtained by the resolution of a min-max optimization problem. However, the performance criterion to be optimized is non-convex. To overcome this problem, non-determinist and determinist global optimization algorithms are proposed namely Genetic Algorithms (GA) and Generalized Geometric Programming (GGP). Experiment results onto a three tanks system are given to illustrate the effectiveness of the developed strategies.
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This work presents an application of linear and nonlinear robust predictive control onto a three tanks system. The design of the linear solution is based on Single-Input Single-Output Controlled Auto Regressive Integrated Moving Average... more
This work presents an application of linear and nonlinear robust predictive control onto a three tanks system. The design of the linear solution is based on Single-Input Single-Output Controlled Auto Regressive Integrated Moving Average (CARIMA) model and the nonlinear controller considers Nonlinear Auto Regressive with eXogenous output (NARX) model. Parametric uncertainties and polytopic uncertainties are adopted in order to take into account the uncertain behavior of the system. Based on worst case strategy, the control law is obtained by the resolution of a min-max optimization problem. However, the performance criterion to be optimized is non-convex. To overcome this problem, non-determinist and determinist global optimization algorithms are proposed namely Genetic Algorithms (GA) and Generalized Geometric Programming (GGP). Experiment results onto a three tanks system are given to illustrate the effectiveness of the developed strategies.
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This paper deals with the single-vehicle pickup and delivery problem with time windows (1-PDPTW). In the 1-PDPTW a vehicle must serve a collection of transportation requests by taking loads from providers to customers satisfying... more
This paper deals with the single-vehicle pickup and delivery problem with time windows (1-PDPTW). In the 1-PDPTW a vehicle must serve a collection of transportation requests by taking loads from providers to customers satisfying precedence, capacity and time constraints. This paper proposes a scientific literature review on the PDPTW and provides a new hybrid evolutionary approach to solve this problem. Our hybrid evolutionary approach uses an evolutionary algorithm, with special genetic operators, tabu search and Pareto dominance method to provide a set of satisfying and feasible solutions to the 1-PDPTW, minimizing the compromise between total travel distance, total waiting time and total tardiness time.
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ABSTRACT
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In digital mobile communication, the non-stationary channel linear modeling become insufficient for channel nonlinear variations. The objective of this work is to select a suitable neural structure for the channel modeling. We present the... more
In digital mobile communication, the non-stationary channel linear modeling become insufficient for channel nonlinear variations. The objective of this work is to select a suitable neural structure for the channel modeling. We present the advantages of a new neural structure, which is the modified Elman network (MEN), applied to digital communication problems such us the channel modeling. By comparison with
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This paper concerns dynamic output feedback design of discrete-time linear switched systems using switched Lyapunov functions (SLF). A new characterization of stability H¿ performance for the switched system under arbitrary switching is... more
This paper concerns dynamic output feedback design of discrete-time linear switched systems using switched Lyapunov functions (SLF). A new characterization of stability H¿ performance for the switched system under arbitrary switching is first given. The various conditions are given through a family of LMI (linear matrix inequalities) parameterized by a scalar variable which offers an additional degree of freedom, enabling, at the expense of a relatively small degree of complexity in the numerical treatment (one line search), to provide better results compared to previous results. The control is defined as a switched dynamic output feedback which guarantees stability and H¿ performance for the closed-loop system. A numerical example is presented to illustrate the effectiveness of the proposed conditions.
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Research Interests: Engineering, Control Theory, Adaptive Control, Nonlinear Control, Parameter estimation, and 11 moreLinear Model, Mathematical Sciences, Advanced manufacturing technology, Chemical reactors, Control Strategy, Temperature Control, Nonlinear system, Batch Reactor, Nonlinear Model, Predictive Control, and Recursive Least Square
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This paper considers the supervision of complex systems by bond graph (BG) and external models (EM). The bond graph is used for the detection and isolation of fault affecting sensors, actuators or physical components of the process. The... more
This paper considers the supervision of complex systems by bond graph (BG) and external models (EM). The bond graph is used for the detection and isolation of fault affecting sensors, actuators or physical components of the process. The external models structure the industrial process, according to several modes of operation (degraded and normal). The switching from one operated mode to another mode is described by a graph called management mode operation graph. It represents the man-machine interface system. The logic functions are controlled by the availability of services and therefore the state of technological components. Thus, the availability of services (necessary for conducting a mission) will be provided by the monitoring algorithm based on BG at management operation graph. The combined representation BG-EM as behavioral and functional modeling for supervision system design is applied to a three tankssystem.
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This paper provides an application of linear and nonlinear multivariable robust predictive control to a three tanks system. The design of the nonlinear solution is based on a Multi-Input Multi-Output Nonlinear Auto Regressive with... more
This paper provides an application of linear and nonlinear multivariable robust predictive control to a three tanks system. The design of the nonlinear solution is based on a Multi-Input Multi-Output Nonlinear Auto Regressive with eXogenous outputs (MIMO-NARX) model and the linear controller considers a MIMO Controlled Auto Regressive Integrated Moving Average (MIMO-CARIMA) model. Polytopic uncertainties and structured uncertainties are adopted in order to take into account the uncertain physical dynamics of the system. Using worst case strategy, the control law is obtained by the resolution of a min-max optimization problem. However, the performance criterion to be optimized is non-convex. A genetic algorithm is adopted to solve the control problem. The efficiency of the developed strategies is illustrated on a three tanks system.
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This paper deals with the Mixed Logical Dynamical (MLD) approach. It allows to model the hybrid systems involved continuous, discrete dynamics and constraints. The resultant system is well-posed for Model Predictive Control (MPC) that is... more
This paper deals with the Mixed Logical Dynamical (MLD) approach. It allows to model the hybrid systems involved continuous, discrete dynamics and constraints. The resultant system is well-posed for Model Predictive Control (MPC) that is used as control strategy for the system. An active Fault Tolerant control is proposed. This approach is illustrated by a tank system. Simulations are performed using the Hysdel compiler to show the efficiently of this formalism and the fault tolerant MPC control.
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The paper deals with a class of linear continuous systems, containing time-varying parameters that have an affine structure. The objective is to establish robust stability and stabilizabilty conditions, allowing separation between the... more
The paper deals with a class of linear continuous systems, containing time-varying parameters that have an affine structure. The objective is to establish robust stability and stabilizabilty conditions, allowing separation between the dynamic and the Lyapunov matrices. Moreover, it is shown that the developed approach brings a larger degree of freedom, than existing ones, due to an added real variable. The problem is thus formulated in terms of Linear Matrix Inequalities. Also, under the robust stabilizability conditions established, state feedback laws that achieve robust stabilization are designed. The obtained results are compared with some of those given in literature and improvements are mentioned. We demonstrate by numerical examples the contribution of the newly proposed analysis and synthesis approach.
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This paper focuses on robust predictive control (RPC) using uncertain controlled auto regressive integrated moving average (CARIMA) model. To take into account the uncertain behavior of physical process, the parametric uncertainties are... more
This paper focuses on robust predictive control (RPC) using uncertain controlled auto regressive integrated moving average (CARIMA) model. To take into account the uncertain behavior of physical process, the parametric uncertainties are considered. The proposed controller is based on worst case strategy. Consequently, the control law is obtained by resolution of a non convex min-max optimization problem. To overcome the drawbacks of classical optimization methods, a global deterministic optimization, generalized geometric programming (GGP), is proposed. This technique is addressed to non convex polynomial problem which the case of most robust and nonlinear control system analysis and design problem. The efficiency of this technique is tested on benchmark functions and compared with LMI and genetic algorithms optimisation methods. Simulation results obtained with an uncertain process are also presented to illustrate the performance of the proposed controller.
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ABSTRACT This paper propose a robust nonlinear unknown input observer "this an extension of the Luenberger observer in unknown inputs" based on first order Taylor expansion. The observer is characterized by its... more
ABSTRACT This paper propose a robust nonlinear unknown input observer "this an extension of the Luenberger observer in unknown inputs" based on first order Taylor expansion. The observer is characterized by its simplicity in the mathematical development can also attack a large class of nonlinear systems without go through a canonical transformation. A systematic method for calculating the gain of the observer is presented (11). The necessary and sufficient conditions for the existence of the observer are given. A numerical example is given to illustrate the attractiveness and the simplicity of the new design procedure. I. INTRODUCTION A fault tolerant system is able of maintaining stability and a degree of performance in the presence of disturbances. These systems are generally classified into two approaches: Passive Fault-Tolerant Control Systems and Active Fault-Tolerant Control Systems. In our case we will consider the Passive Fault-Tolerant Control Systems.