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Volume 2017, Article ID 5813192, 11 pages
Research Article

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Laboratory of Engineering of Industrial Systems and Renewable Energy, National High School of Engineers of Tunis (ENSIT), 5 Av. Taha Hussein, BP 56-1008, Tunis, Tunisia

Correspondence should be addressed to Adel Taieb; rf.evil@ledabieat

Received 16 April 2017; Revised 18 June 2017; Accepted 22 August 2017; Published 9 October 2017

Academic Editor: Carla Pinto

Copyright © 2017 Adel Taieb et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.