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Applied Computational Intelligence and Soft Computing
Volume 2015 (2015), Article ID 437943, 15 pages
Research Article

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

1SET Laboratory, Electronics Department, University of Blida 1, Route de Soumaa, BP 270, 09000 Blida, Algeria
2High School of Computer Sciences (HEB-ESI), Rue Royale 67, 1000 Brussels, Belgium

Received 26 September 2014; Revised 24 April 2015; Accepted 24 April 2015

Academic Editor: Shyi-Ming Chen

Copyright © 2015 Oussama Ait Sahed 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.


A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.