New approach of model based T-S fuzzy predictive control using LMI approach

Author

Zahaf, A. ; Boutamina, B. ; Bououden, S. ; Filali, S.

Author_Institution

Dept. of Electron., Univ. of Constantine 1, Constantine, Algeria

fYear

2014

Firstpage

38

Lastpage

43

Abstract

In this work, a model based T-S fuzzy predictive control LMI optimization is introduced. The aim of discrete T-S fuzzy predictive controller is to drive the state of the system to the original state where a stabilizing controller is ensured. The stability of the controlled systems is studied using non quadratic case of the Lyapunov function and adopting of Non-PDC controller. The stability is guaranteed based on the conditions expressed of terms of LMIs. The optimal solution has been obtained at each sampling time. The results are shows the effectiveness of this strategy.

Keywords

Lyapunov methods; discrete systems; fuzzy control; linear matrix inequalities; optimisation; predictive control; LMI optimization; Lyapunov function; controlled systems; discrete T-S fuzzy predictive controller; model based T-S fuzzy predictive control; nonPDC controller; optimal solution; stabilizing controller; Fuzzy systems; Lyapunov methods; Nonlinear systems; Optimization; Predictive control; Predictive models; Stability analysis; Linear matrix inequality (LMI); Model predictive control (MPC); Non-Parallel distributed compensation Non-PDC; Takagi-Sugeno (T-S) fuzzy systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2014 15th International Conference on

Type

conf

DOI

10.1109/STA.2014.7086708

Filename

7086708