Lotfi Chaouech

A fuzzy sliding mode observer for the nonlinear uncertain system based on T-S model

This paper addressed the analysis and design of a sliding mode observer for a class of uncertain nonlinear systems. We continue to work in [1] that proposed an approach to design the sliding mode control of same system using the control law obtained to analyze our sliding mode observer. The main idea of the paper is the development of a robust observer with respect to the uncertainties parametric type norm bounded as well as the synthesis of sufficient stability conditions of this observer. The stabilization of the observer is performed by the search of suitable Lyapunov matrices. It is shown how to determine the gains of the local observers, these gains being solutions of a set of linear matrix inequalities (LMI). A numerical application of inverted pendulum is given to validate the theoretical results of our approach.

Design of sliding mode control of nonlinear system based on Takagi-Sugeno fuzzy model

In this paper, a sliding mode control algorithm based on Takagi-Sugeno (T-S) fuzzy model for a class of nonlinear systems is discussed. For a complex physical system represented by an aggregated fuzzy global model which compromises a set of linear models, conditions for the fuzzy sliding mode control to stabilize the global fuzzy model are given. Firstly, we choose the sliding surface which gives a good behaviour during sliding mode. It is formulated as an assignment of the poles of uncertain nonlinear system in a convex optimization area. Secondly, we design a nonlinear control law leading the state trajectory on the sliding surface in a finite time. A numerical application of a flexible joint manipulator is given to validate the theoretical results of our approach.

Design of new fuzzy sliding mode controller based on parallel distributed compensation controller and using the scalar sign function

This paper presents a new design of fuzzy sliding mode controller based on parallel distributed compensation and using a scalar sign function. The proposed fuzzy sliding mode controller (FSMC) uses the parallel distributed compensation (PDC) scheme to design the state feedback control law. The controller gains are determined in offline mode via linear quadratic regulator technique. Moreover, the fuzzy sliding surface of the system is designed using stable eigenvectors and the scalar sign function in order to overcome the discontinuous switching. This later is obtained by a sign function of the standard FSMC. The advantages of the proposed design are a minimum energy control effort, faster response and zero steady-state error. Finally, the validity of the proposed design strategy is demonstrated through the simulation of a flexible joint robot.

Sliding mode control of nonlinear uncertain system based on Takagi-Sugeno fuzzy model

This paper proposes an approach to design sliding mode control for a class of uncertain nonlinear systems, where the uncertainty is a norm bounded type. Firstly, we choose the sliding surface which gives a good behaviour during sliding mode. It is formulated as an assignment of the poles of uncertain nonlinear system in a convex optimization area. Secondly, we design a nonlinear control law leading the state trajectory on the sliding surface in a finite time. A numerical application of inverted pendulum is given to validate the theoretical results of our approach.

Parameter estimation of nonlinear systems using a robust possibilistic c-regression model algorithm

This article studies the problem of inappropriate parameter estimation for nonlinear system when the dataset is contaminated by noise based on fuzzy c-regression models. In comparison to the existing algorithms in the literature, the proposed method uses a generalized objective function that reduces the errors of partitioning datasets contaminated by noise, and as a consequence an accurate model is obtained. Indeed, it combines a modified version of possibilistic c-means procedure with fuzzy c-regression models. The weighted least squares method is exploited to identify the parameters contained in the consequent (THEN part). The results of this study demonstrate the effectiveness of the proposed method compared with other extended versions of the fuzzy c-regression model algorithm such as modified fuzzy c-regression model algorithm, possibilistic c-regression model and interval type-2 fuzzy c-regression model algorithm as well as other techniques existing in the literature.

Fuzzy Sliding Mode Controller Design Based on Euclidean Particle Swarm Optimization

The main motivation for this work is to develop a new design of fuzzy sliding mode control based on parallel distributed compensator and using Euclidean particle swarm optimization in order to overcome the problem caused by an inappropriate selection of sliding surface parameters. The proposed method employs the parallel distributed compensator scheme to design the state feedback based control law. The controller gains are determined in offline mode via a linear quadratic regular. The Euclidean particle swarm optimization is incorporated into the linear quadratic regular technique for determining the optimal weight matrices. Consequently, an optimal sliding surface is obtained. This latter is used to design the proposed control law. Finally, several tests have been done to examine the performance and applicability of the proposed method in real world.

Robust possibilistic c-regression models algorithm

This paper studies the problem of the parameter identification based on fuzzy c-regression models for nonlinear systems. The novel procedure combines the possibilistic c-means procedure with fuzzy c-regression models (FCRM) in order to reduce the effects of noisy data. In comparison to the existing algorithms in the literature, the proposed method utilizes a generalized objective function that reduces the errors of partitioning data sets contaminated by noise and as a consequence an accurate model is obtained. The results of this study demonstrate the effectiveness of proposed method compared with other extended versions of FCRM algorithm.