To read the full version of this content please select one of the options below:

Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints

Adel Taeib (Department of Electrical engineering, High School of Sciences and Engineering of Tunis (ESSTT), Tunis, Tunisia)
Moêz Soltani (Department of Electrical engineering, Mateur High Institute of Applied Science and Technology (ISSAT Mateur), Mateur, Tunisia)
Abdelkader Chaari (Department of Electrical engineering, High School of Sciences and Engineering of Tunis (ESSTT), Tunis, Tunisia)

Kybernetes

ISSN: 0368-492X

Article publication date: 3 November 2014

Downloads
234

Abstract

Purpose

The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor.

Design/methodology/approach

A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints.

Findings

The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller.

Originality/value

This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

Keywords

Citation

Taeib, A., Soltani, M. and Chaari, A. (2014), "Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints", Kybernetes, Vol. 43 No. 9/10, pp. 1469-1482. https://doi.org/10.1108/K-06-2013-0103

Publisher

:

Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited