Paper Info
Title
Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints.
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.
Year
DOI
Venue
2014
10.1108/K-06-2013-0103
KYBERNETES
Keywords
Field
DocType
Control systems,Optimization techniques,Fuzzy logic,Nonlinear systems
Particle swarm optimization,Step response,Mathematical optimization,Control theory,Computer science,Control theory,Fuzzy logic,Model predictive control,System dynamics,Control system,Initialization
Journal
Volume
Issue
ISSN
43
9-10
0368-492X
Citations 
PageRank 
References 
2
0.40
10
Authors
3
Name
Order
Citations
PageRank
Adel Taeib142.38
Moêz Soltani2285.05
Abdelkader Chaari3264.92