Abdelaziz Hamzaoui

Second-Order Sliding Fuzzy Interval Type-2 Control for an Uncertain System With Real Application

A new second-order sliding-mode type-2 fuzzy controller for nonlinear uncertain perturbed systems is developed in this paper. To overcome the constraint on the knowledge of the system model, we have used local models that are related to some operating points to synthesize a type-2 nominal fuzzy global model. The control is based on the super-twisting algorithm, which is among second-order sliding-mode controls (SMCs). Moreover, two adaptive fuzzy type-2 systems have been introduced to generate the two super-twisting signals to avoid both the chattering and the constraint on the knowledge of disturbances and uncertainties upper bounds. These adaptive fuzzy type-2 systems are adjusted online by two adaptation laws that are deduced from the stability analysis in the Lyapunov sense. It has only one input, i.e., the sliding surface, and one output, i.e., the optimal values of the gains control, which are hard to compute with the original algorithm. Many results of the one-link manipulator are obtained: first by the simulation in order to compare the performances of the proposed method with that given by Levant and then in a real-time application in order to confirm the efficiency of the proposed approach. The experimentation and simulation are done for the tracking control problem.

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.

State observer based robust adaptive fuzzy controller for nonlinear uncertain and perturbed systems

A robust adaptive fuzzy controller, based on a state observer, for a nonlinear uncertain and perturbed system is presented. The state observer is introduced to resolve the problem of the unavailability of the state variables. Two control signals are added to a basic state feedback control law, deduced from a nominal model, to guarantee the tracking performance in the presence of structural uncertainties and external disturbances. The first control signal is computed from an adaptive fuzzy system and eliminates the effect of structural uncertainties and estimation errors. Updating the adjustable parameters is ensured by a PID law to obtain a fast convergence. Robustness of the closed-loop system is guaranteed by an H/spl infin/ supervisor computed from a Riccati type equation. Simulation example is presented to show the efficiency of the proposed method.

Implementation of a new fuzzy vector control of induction motor

The aim of this paper is to present a new approach to control an induction motor using type-1 fuzzy logic. The induction motor has a nonlinear model, uncertain and strongly coupled. The vector control technique, which is based on the inverse model of the induction motors, solves the coupling problem. Unfortunately, in practice this is not checked because of model uncertainties. Indeed, the presence of the uncertainties led us to use human expertise such as the fuzzy logic techniques. In order to maintain the decoupling and to overcome the problem of the sensitivity to the parametric variations, the field-oriented control is replaced by a new block control. The simulation results show that the both control schemes provide in their basic configuration, comparable performances regarding the decoupling. However, the fuzzy vector control provides the insensitivity to the parametric variations compared to the classical one. The fuzzy vector control scheme is successfully implemented in real-time using a digital signal processor board dSPACE 1104. The efficiency of this technique is verified as well as experimentally at different dynamic operating conditions such as sudden loads change, parameter variations, speed changes, etc. The fuzzy vector control is found to be a best control for application in an induction motor.

An improved robust adaptive fuzzy controller for MIMO systems

This paper addresses robust fuzzy adaptive control for nonlinear multi-input multi-output systems in the presence of parametric uncertainties and external disturbances. A universal approximator is used to approximate the plant model. The boundedness of the variables involved and convergence towards zero of the tracking error are guaranteed by a supervisory control. The effect of both the approximation errors and the external disturbances is attenuated to a prescribed level via an H∞ supervisor. Sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability. The effectiveness of the proposed controller design methodology is demonstrated by numerical simulations of a two-link robot.

Shifting nonlinear phenomena in a DC-DC converter using a fuzzy logic controller

The occurrence of nonlinear phenomena in DC-DC power converters makes their analysis and control difficult. We propose a converter model that takes into account the internal resistances of the converter elements. A fuzzy logic controller is then used to shift the nonlinear phenomena outside the operating domain, and to ensure current regulation over a wide range of parameters. To validate the achieved performance, a comparative study is presented.

A SUPERVISORY ROBUST ADAPTIVE FUZZY CONTROLLER

Abstract A fuzzy controller equipped with an adaptive algorithm and two supervisors is developed in this work to achieve tracking performances for a class of uncertain nonlinear single input single output (SISO) systems with external disturbances. The convergence of the training algorithm is guarantied by a gradient projection law. The effect of both the approximation errors and the external disturbances is attenuated to a prescribed level thanks to H∞ control. The convergence of the tracking error toward zero is guarantied by a supervisor where linguistic rules are used to accelerate the convergence speed.

MIMO second order sliding mode fuzzy type-2 control

This paper deals with the synthesis of a second order sliding mode type-2 fuzzy controller for an nth order multi input multi output (MIMO) nonlinear systems suffering from model uncertainties and subjected to external perturbation. To overcome the constraint on the knowledge of the system model, local models related to some operating points were used to synthesize a type-2 nominal fuzzy model. For sliding modes, adaptive fuzzy type-2 systems have been introduced to generate the super twisting signals to avoid both the chattering and the constraint on the knowledge of upper bounds disturbances and uncertainties. These adaptive fuzzy type-2 systems are adjusted on-line by adaptation laws deduced from the stability analysis in Lyapunov sense. Many simulation results for two link robot manipulator are given to illustrate the good tracking performances, the robustness of the closed loop system and smooth signal control obtained by the proposed approach.

Fuzzy sliding mode control for a class of non-linear continuous systems

In this work, a fuzzy sliding mode control for a non-linear uncertain and disturbed system is presented. Two adaptive fuzzy systems are used to determine the equivalent control signal. To overcome the design constraints in the classical sliding mode control, an adaptive fuzzy system and an attenuation term are introduced in the control law. A PID adaptation law is used to update the adjustable parameters of this fuzzy system. The synthesised controller efficiently eliminates the chattering phenomenon without requiring any particular knowledge about the upper bounds of either the external disturbances or the approximation errors. The simulation results illustrate the tracking performances of the proposed method.

ADAPTIVE VARIABLE STRUCTURE FUZZY WAVELET NETWORK BASED CONTROLLER FOR NONLINEAR SYSTEMS

Abstract this paper presents a novel adaptive variable structure fuzzy wavelet network based controller for nonlinear systems. The proposed controller combines the advantages of the fuzzy wavelet networks and those of sliding mode control. Indeed, due to its ability to approximate nonlinear function and its fast convergence, a fuzzy wavelet network system is used as approximator to generate a control signal. To reduce the starting energy caused by the presence of approximation errors and external disturbances, a variable sliding surface has been adopted. The constructed control allows guaranteeing good tracking performances and the robustness of the closed loop system. For illustrating the efficiency of the proposed approach, a simulation example is presented.