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Khaled Belarbi

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IEEE/CAA J. Autom. Sinica

Dr. Ashraf M M Aboshosha

Khier Benmahammed

Khier Benmahammed

Mohammed M’Saad

Ecole Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN)

Poonam Upadhyay

Abdelbaki Djouambi

OUASSILA BOUREBIA

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Papers by Khaled Belarbi

Multivariable Fuzzy Logic Controller Based on a Compensator of Interactions and Genetic Tuningr

A fuzzy controller for multivariable systems based on fuzzy controllers for the decoupled monovar... more A fuzzy controller for multivariable systems based on fuzzy controllers for the decoupled monovariable subsystems and a fuzzy compensator of the interactions is introduced. The fuzzy compensator acts as a feedforward controller. It has as inputs the outputs of the monovariable controllers considered as known perturbations and produces compensation signals which are added to the single loop control signals for minimizing the effect of the interactions. The proposed algorithm improves the interpretability of a fuzzy multivariable Mamdani fuzzy controller. A genetic algorithm is used for determining the parameters of the fuzzy controller and compensator. The performance of the methodology is analyzed on two systems: a benchmark model of a distillation column and two-link manipulators.

Adaptive Control of Nonaffine Nonlinear Systems by Neural state Feedback

In this paper, a new control method for a class of single input single output nonaffine nonlinear... more In this paper, a new control method for a class of single input single output nonaffine nonlinear systems is considered using radial basis function (RBF) neural networks (NNs). Firstly, the existence of an ideal implicit feedback linearization control is established based on implicit function theory. An online RBF system is introduced to approximate this ideal implicit feedback linearization law. The proposed neural fuzzy adaptive controller ensures that the system output tracks a given bounded reference signal, while the closed loop stability results are provided and guaranteed using Lyapunov theory. The effectiveness of the proposed controller is illustrated through a simulation to a nonaffine nonlinear system.

An adaptive fuzzy predictive control of nonlinear processes based on Multi-Kernel least squares support vector regression

Applied Soft Computing, Dec 1, 2018

1) An adaptive Takagi-Sugeno system based on Multi-Kernel Least Squares Support Vector Regression... more 1) An adaptive Takagi-Sugeno system based on Multi-Kernel Least Squares Support Vector Regression (adaptive TS-LSSVR) was introduced for system identification. 2) The adaptive TS-LSSVR system was integrated within the generalized predictive controller (GPC). 3) The proposed adaptive TS-LSSVR GPC controller was used to control nonlinear systems. 4) The adaptive TS-LSSVR GPC controller has shown good performance in controlling nonlinear systems. Also, the proposed controller copes well with additional disturbances.

Model Reference Neural-Fuzzy Adaptive Control of the Concentration in a Chemical Reactor (CSTR)

IFAC-PapersOnLine, 2016

In this work, we consider the application of a special type of neural networks based on radial ba... more In this work, we consider the application of a special type of neural networks based on radial basis functions (RBF) which belongs to a class of associative memory neural networks. The RBF neural network is used in a novel approach as a controller in a model reference adaptive control (MRAC) architecture. The global control scheme is based on a one step ahead fuzzy model and the objective is to control a Continuous Stared Tank Reactor (CSTR) complex nonlinear system.

Stable Adaptive Control for a Class of Nonlinear Systems Without Use of a Supervisory Term in the Control Law

DOAJ (DOAJ: Directory of Open Access Journals), Feb 1, 2012

In this paper, a direct adaptive control scheme for a class of nonlinear systems is proposed. The... more In this paper, a direct adaptive control scheme for a class of nonlinear systems is proposed. The architecture employs a Gaussian radial basis function (RBF) network to construct an adaptive controller. The parameters of the adaptive controller are adapted and changed according to a law derived using Lyapunov stability theory. The centres of the RBF network are adapted on line using the kmeans algorithm. Asymptotic Lyapunov stability is established without the use of a supervisory (compensatory) term in the control law and with the tracking errors converging to a neighbourhood of the origin. Finally, a simulation is provided to explore the feasibility of the proposed neuronal controller design method.

A fuzzy approximator for model based predictive control

In this work, we consider the approximation of the model predictive controllers using a fuzzy Tak... more In this work, we consider the approximation of the model predictive controllers using a fuzzy Takagi Sugeno system and invoking its universal approximating property. The first step of the procedure is to generate the data for the approximation. This is performed in simulation off line by applying the MBPC to the system and recording the MBPC controller values for each sampling instant. The MBPC optimisation is solved using the particle swarm optimisation metaheuristic. Once the data are collected for various initial conditions, the consequence parameters of the TS fuzzy system are optimised so as it can approximate the output of the MBPC controller. The obtained Fuzzy approximator is then used for controlling the system in various conditions. The procedure is applied in simulation for two systems. The results are very interesting and worth of further investigation.

A Fuzzy Control of the Instabilities of an Axial Flow Compressor

This paper describes the active control of the instabilities of an axial flow compressor using a ... more This paper describes the active control of the instabilities of an axial flow compressor using a fuzzy logic controller. The controller is designed using a genetic algorithm. The control variable is the throttle of the outlet valve, situated after the compressor. Simulation results show that the controller is able to control both the surge and the rotating stall in the critical operating point, thus increasing the region of stability.

Fuzzy and Neural Adaptive Control of a Class of Nonlinear Systems

A Neural Network Controller Optimised with Multi Objective Genetic Algorithms for a Laboratory Anti-Lock Braking System

Sciences & Technologie. B, Sciences de l'ingénieur, Jun 1, 2012

In this work, we consider the design of a neural network controller for the ABS laboratory system... more In this work, we consider the design of a neural network controller for the ABS laboratory system witch’s highly non linear. The objective is to control the wheel slip. The controller is designed off-line using a multi-objective optimisation process solved using a multi objective genetic algorithms. The objective of the design process is to find a satisfactory controller with a reasonable structure. The structure is defined as the number of input variables and the number of neurons in the hidden layer. Thus the multi objective genetic algorithms has to minimize three objectives: the number of neurons in the hidden layer, the error which is the difference between the wheel slip reference and the real wheel slip and the third objective is the number and type of inputs to the network. The results of simulation are encouraging.

Real Time Tracking Trajectory and Obstacle Avoidance for Two Mobile Robots by FLC and NMPC

International Journal on Electrical Engineering and Informatics, Dec 30, 2021

Fast solution procedures are still persist as a challenge for autonomous mobile robot control for... more Fast solution procedures are still persist as a challenge for autonomous mobile robot control for tracking trajectory, while simultaneously detecting and avoiding static and dynamic obstacles. In this work, two fast controllers are investigated: the Fuzzy Logic Controller (FLC) and the NMPC based on the Knowledge Particle Swarm Optimization (KPSO). Theses controllers are used to control two mobile robots at the same time for trajectory tracking and obstacle avoidance in a dynamical environment. Simulation and experimental results are presented for the studied controllers. The results show that the FLC has the fastest computation time with an acceptable accuracy in tracking trajectory and obstacle avoidance while the control signals smoothness and robots navigation accuracy are very good with the NMPC-KPSO. In this latter case, the computation of the control signal needs to be shortened.

Fuzzy Adaptive Control of Dissolved Oxygen in a Waste Water Treatment Process

IFAC-PapersOnLine, 2015

The general idea behind the wastewater treatment procedure is to obtain an effluent having the su... more The general idea behind the wastewater treatment procedure is to obtain an effluent having the substrate concentration within some standard limits. This purpose can be satisfied by controlling the concentration of dissolved oxygen to a certain value. In this work, we consider a simulation study of a fuzzy adaptive control for a concentration in a bioreactor. A Takagi Sugeno (TS) fuzzy inference system (FIS) is used to approximate the feedback linearization law, and the parameters are updated based on a fuzzy estimation. The test system is a wastewater treatment plant and the objective is to control the dissolved oxygen (DO) concentration levels in the activated sludge process which is one of the major processes in a wastewater treatment plant. The results obtained are compared with other works.

Design of Fuzzy Observers and Output Feedback Fuzzy Controllers for Takagi–Sugeno Discrete Systems Via the Matrices Norms Approach

Journal of Control, Automation and Electrical Systems

Infinite horizon fuzzy optimal control: optimality does not imply asymptotic stability

International Journal of Electrical and Computer Engineering (IJECE)

In this paper, novel stability result for discrete-time infinite horizon optimal control using fu... more In this paper, novel stability result for discrete-time infinite horizon optimal control using fuzzy objective functions is presented. For this class of control, the fuzzy goals and the fuzzy constraints introduced in the fuzzy objective function handle the constraints placed on both the state and the control vectors. We analyze the asymptotic stability of the equilibrium for the infinite horizon fuzzy optimal control law using the minimum aggregation operator. We show that the infinite horizon control with the minimum aggregation operator does not guarantee the asymptotic stability of the equilibrium in general. This is done by deriving an analytical solution of the control law for a simple linear system using a fuzzy dynamic programming approach. An example that shows the novel asymptotic stability result of the equilibrium for discrete-time infinite horizon optimal control with fuzzy objective function problem is given.

An Alternative Stabilization Approach for Delayed Takagi-Sugeno Discrete Fuzzy Systems

2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

Commande et identification par les ondelettes floues et optimisation par les algorithmes evolutionnaires

Comparaison of Heuristic and Hybrid Heuristic for the Solution of Linear Model Based Predictive Control

Contribution à la commande prédictive non linéaire

Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control

World Journal of Engineering, 2017

Purpose Rapid solution methods are still a challenge for difficult optimization problems among th... more Purpose Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods. Originality/value The computation time is less than 0.02 ms...

On Matrix Norms, Stability and Stabilization of a Class of Discrete Takagi–Sugeno Fuzzy Systems

IEEE Transactions on Fuzzy Systems, 2019

Based on a result of stability for linear time varying systems, a sufficient condition for global... more Based on a result of stability for linear time varying systems, a sufficient condition for global stability, that does not use Lyapunov theory, is established for a class of discrete Takagi Sugeno fuzzy systems. The condition involves testing the norms of products of the matrices of the consequences. The approach seeks to determine if at an instant of time the system becomes a contraction mapping. Because the computation burden associated with these tests may become prohibitive, a necessary and sufficient condition for local stability is derived. In a way, this decreases the computational burden and allows to customize the test. A two-step iterative stabilization algorithm is then introduced. The problem involves norm minimization and a posteriori stability test. Numerical examples are presented to demonstrate the applicability of the approach.

Comparison of Neural Networks and Fuzzy Logic Control Designed by Multi-objective Genetic aWlgorithm

International Journal of Advancements in Computing Technology, 2011

In this work we consider the design of neural network and Takagi Sugeno fuzzy logic controller, T... more In this work we consider the design of neural network and Takagi Sugeno fuzzy logic controller, TSFLC, with Multi-Obejctive Genetic Algorithm, MOGA. For the neural network, the MOGA has to minimize three objectives, the cumulated error, the number of neurons in the hidden layer and the number and type of inputs to the network. In the case of the TS FLC, the objectives are the cumulated error and the parameters of the rules consequence. Both algorithms are applied for the control of temperature system.

Multivariable Fuzzy Logic Controller Based on a Compensator of Interactions and Genetic Tuningr

A fuzzy controller for multivariable systems based on fuzzy controllers for the decoupled monovar... more A fuzzy controller for multivariable systems based on fuzzy controllers for the decoupled monovariable subsystems and a fuzzy compensator of the interactions is introduced. The fuzzy compensator acts as a feedforward controller. It has as inputs the outputs of the monovariable controllers considered as known perturbations and produces compensation signals which are added to the single loop control signals for minimizing the effect of the interactions. The proposed algorithm improves the interpretability of a fuzzy multivariable Mamdani fuzzy controller. A genetic algorithm is used for determining the parameters of the fuzzy controller and compensator. The performance of the methodology is analyzed on two systems: a benchmark model of a distillation column and two-link manipulators.

Adaptive Control of Nonaffine Nonlinear Systems by Neural state Feedback

In this paper, a new control method for a class of single input single output nonaffine nonlinear... more In this paper, a new control method for a class of single input single output nonaffine nonlinear systems is considered using radial basis function (RBF) neural networks (NNs). Firstly, the existence of an ideal implicit feedback linearization control is established based on implicit function theory. An online RBF system is introduced to approximate this ideal implicit feedback linearization law. The proposed neural fuzzy adaptive controller ensures that the system output tracks a given bounded reference signal, while the closed loop stability results are provided and guaranteed using Lyapunov theory. The effectiveness of the proposed controller is illustrated through a simulation to a nonaffine nonlinear system.

An adaptive fuzzy predictive control of nonlinear processes based on Multi-Kernel least squares support vector regression

Applied Soft Computing, Dec 1, 2018

1) An adaptive Takagi-Sugeno system based on Multi-Kernel Least Squares Support Vector Regression... more 1) An adaptive Takagi-Sugeno system based on Multi-Kernel Least Squares Support Vector Regression (adaptive TS-LSSVR) was introduced for system identification. 2) The adaptive TS-LSSVR system was integrated within the generalized predictive controller (GPC). 3) The proposed adaptive TS-LSSVR GPC controller was used to control nonlinear systems. 4) The adaptive TS-LSSVR GPC controller has shown good performance in controlling nonlinear systems. Also, the proposed controller copes well with additional disturbances.

Model Reference Neural-Fuzzy Adaptive Control of the Concentration in a Chemical Reactor (CSTR)

IFAC-PapersOnLine, 2016

In this work, we consider the application of a special type of neural networks based on radial ba... more In this work, we consider the application of a special type of neural networks based on radial basis functions (RBF) which belongs to a class of associative memory neural networks. The RBF neural network is used in a novel approach as a controller in a model reference adaptive control (MRAC) architecture. The global control scheme is based on a one step ahead fuzzy model and the objective is to control a Continuous Stared Tank Reactor (CSTR) complex nonlinear system.

Stable Adaptive Control for a Class of Nonlinear Systems Without Use of a Supervisory Term in the Control Law

DOAJ (DOAJ: Directory of Open Access Journals), Feb 1, 2012

In this paper, a direct adaptive control scheme for a class of nonlinear systems is proposed. The... more In this paper, a direct adaptive control scheme for a class of nonlinear systems is proposed. The architecture employs a Gaussian radial basis function (RBF) network to construct an adaptive controller. The parameters of the adaptive controller are adapted and changed according to a law derived using Lyapunov stability theory. The centres of the RBF network are adapted on line using the kmeans algorithm. Asymptotic Lyapunov stability is established without the use of a supervisory (compensatory) term in the control law and with the tracking errors converging to a neighbourhood of the origin. Finally, a simulation is provided to explore the feasibility of the proposed neuronal controller design method.

A fuzzy approximator for model based predictive control

In this work, we consider the approximation of the model predictive controllers using a fuzzy Tak... more In this work, we consider the approximation of the model predictive controllers using a fuzzy Takagi Sugeno system and invoking its universal approximating property. The first step of the procedure is to generate the data for the approximation. This is performed in simulation off line by applying the MBPC to the system and recording the MBPC controller values for each sampling instant. The MBPC optimisation is solved using the particle swarm optimisation metaheuristic. Once the data are collected for various initial conditions, the consequence parameters of the TS fuzzy system are optimised so as it can approximate the output of the MBPC controller. The obtained Fuzzy approximator is then used for controlling the system in various conditions. The procedure is applied in simulation for two systems. The results are very interesting and worth of further investigation.

A Fuzzy Control of the Instabilities of an Axial Flow Compressor

This paper describes the active control of the instabilities of an axial flow compressor using a ... more This paper describes the active control of the instabilities of an axial flow compressor using a fuzzy logic controller. The controller is designed using a genetic algorithm. The control variable is the throttle of the outlet valve, situated after the compressor. Simulation results show that the controller is able to control both the surge and the rotating stall in the critical operating point, thus increasing the region of stability.

Fuzzy and Neural Adaptive Control of a Class of Nonlinear Systems

A Neural Network Controller Optimised with Multi Objective Genetic Algorithms for a Laboratory Anti-Lock Braking System

Sciences & Technologie. B, Sciences de l'ingénieur, Jun 1, 2012

In this work, we consider the design of a neural network controller for the ABS laboratory system... more In this work, we consider the design of a neural network controller for the ABS laboratory system witch’s highly non linear. The objective is to control the wheel slip. The controller is designed off-line using a multi-objective optimisation process solved using a multi objective genetic algorithms. The objective of the design process is to find a satisfactory controller with a reasonable structure. The structure is defined as the number of input variables and the number of neurons in the hidden layer. Thus the multi objective genetic algorithms has to minimize three objectives: the number of neurons in the hidden layer, the error which is the difference between the wheel slip reference and the real wheel slip and the third objective is the number and type of inputs to the network. The results of simulation are encouraging.

Real Time Tracking Trajectory and Obstacle Avoidance for Two Mobile Robots by FLC and NMPC

International Journal on Electrical Engineering and Informatics, Dec 30, 2021

Fast solution procedures are still persist as a challenge for autonomous mobile robot control for... more Fast solution procedures are still persist as a challenge for autonomous mobile robot control for tracking trajectory, while simultaneously detecting and avoiding static and dynamic obstacles. In this work, two fast controllers are investigated: the Fuzzy Logic Controller (FLC) and the NMPC based on the Knowledge Particle Swarm Optimization (KPSO). Theses controllers are used to control two mobile robots at the same time for trajectory tracking and obstacle avoidance in a dynamical environment. Simulation and experimental results are presented for the studied controllers. The results show that the FLC has the fastest computation time with an acceptable accuracy in tracking trajectory and obstacle avoidance while the control signals smoothness and robots navigation accuracy are very good with the NMPC-KPSO. In this latter case, the computation of the control signal needs to be shortened.

Fuzzy Adaptive Control of Dissolved Oxygen in a Waste Water Treatment Process

IFAC-PapersOnLine, 2015

The general idea behind the wastewater treatment procedure is to obtain an effluent having the su... more The general idea behind the wastewater treatment procedure is to obtain an effluent having the substrate concentration within some standard limits. This purpose can be satisfied by controlling the concentration of dissolved oxygen to a certain value. In this work, we consider a simulation study of a fuzzy adaptive control for a concentration in a bioreactor. A Takagi Sugeno (TS) fuzzy inference system (FIS) is used to approximate the feedback linearization law, and the parameters are updated based on a fuzzy estimation. The test system is a wastewater treatment plant and the objective is to control the dissolved oxygen (DO) concentration levels in the activated sludge process which is one of the major processes in a wastewater treatment plant. The results obtained are compared with other works.

Design of Fuzzy Observers and Output Feedback Fuzzy Controllers for Takagi–Sugeno Discrete Systems Via the Matrices Norms Approach

Journal of Control, Automation and Electrical Systems

Infinite horizon fuzzy optimal control: optimality does not imply asymptotic stability

International Journal of Electrical and Computer Engineering (IJECE)

In this paper, novel stability result for discrete-time infinite horizon optimal control using fu... more In this paper, novel stability result for discrete-time infinite horizon optimal control using fuzzy objective functions is presented. For this class of control, the fuzzy goals and the fuzzy constraints introduced in the fuzzy objective function handle the constraints placed on both the state and the control vectors. We analyze the asymptotic stability of the equilibrium for the infinite horizon fuzzy optimal control law using the minimum aggregation operator. We show that the infinite horizon control with the minimum aggregation operator does not guarantee the asymptotic stability of the equilibrium in general. This is done by deriving an analytical solution of the control law for a simple linear system using a fuzzy dynamic programming approach. An example that shows the novel asymptotic stability result of the equilibrium for discrete-time infinite horizon optimal control with fuzzy objective function problem is given.

An Alternative Stabilization Approach for Delayed Takagi-Sugeno Discrete Fuzzy Systems

2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

Commande et identification par les ondelettes floues et optimisation par les algorithmes evolutionnaires

Comparaison of Heuristic and Hybrid Heuristic for the Solution of Linear Model Based Predictive Control

Contribution à la commande prédictive non linéaire

Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control

World Journal of Engineering, 2017

Purpose Rapid solution methods are still a challenge for difficult optimization problems among th... more Purpose Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods. Originality/value The computation time is less than 0.02 ms...

On Matrix Norms, Stability and Stabilization of a Class of Discrete Takagi–Sugeno Fuzzy Systems

IEEE Transactions on Fuzzy Systems, 2019

Based on a result of stability for linear time varying systems, a sufficient condition for global... more Based on a result of stability for linear time varying systems, a sufficient condition for global stability, that does not use Lyapunov theory, is established for a class of discrete Takagi Sugeno fuzzy systems. The condition involves testing the norms of products of the matrices of the consequences. The approach seeks to determine if at an instant of time the system becomes a contraction mapping. Because the computation burden associated with these tests may become prohibitive, a necessary and sufficient condition for local stability is derived. In a way, this decreases the computational burden and allows to customize the test. A two-step iterative stabilization algorithm is then introduced. The problem involves norm minimization and a posteriori stability test. Numerical examples are presented to demonstrate the applicability of the approach.

Comparison of Neural Networks and Fuzzy Logic Control Designed by Multi-objective Genetic aWlgorithm

International Journal of Advancements in Computing Technology, 2011

In this work we consider the design of neural network and Takagi Sugeno fuzzy logic controller, T... more In this work we consider the design of neural network and Takagi Sugeno fuzzy logic controller, TSFLC, with Multi-Obejctive Genetic Algorithm, MOGA. For the neural network, the MOGA has to minimize three objectives, the cumulated error, the number of neurons in the hidden layer and the number and type of inputs to the network. In the case of the TS FLC, the objectives are the cumulated error and the parameters of the rules consequence. Both algorithms are applied for the control of temperature system.