Journal
INFORMATION SCIENCES
Volume 299, Issue -, Pages 143-158Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2014.11.050
Keywords
Adaptive fuzzy system; Predictive control; Ant colony optimization; Parameters optimization
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Funding
- Norway by Innovation Norges Forskningsraad-Norwegian Research Council
- France by the 'Ministere des Affaires Etrangeres'
- 'Ministere de l'Enseignement Superieur et de la Recherche' (MESR) within the Aurora program.
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In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with proportional integral-ant colony optimization controller and adaptive fuzzy model predictive controller. (C) 2014 Elsevier Inc. All rights reserved.
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