6533b7d8fe1ef96bd126b7e5

RESEARCH PRODUCT

Fuzzy predictive controller design using ant colony optimization algorithm

Hamid Reza KarimiSofiane BououdenMohammed Chadli

subject

EngineeringMeta-optimizationOptimization problemLinear programmingbusiness.industryAnt colony optimization algorithmsComputer Science Applications1707 Computer Vision and Pattern RecognitionComputingMethodologies_ARTIFICIALINTELLIGENCEFuzzy logicModel predictive controlControl theoryControl and Systems EngineeringModeling and SimulationModeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Control and Systems Engineering; Electrical and Electronic EngineeringElectrical and Electronic EngineeringbusinessAlgorithmMetaheuristic

description

In this paper, an approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the Ant Colony Optimization (ACO) is studied. On-line adaptive fuzzy identification is used to identify the system parameters. These parameters are used to calculate the objective function based on predictive approach and structure of RST control. The optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to calculate a sequence of future RST control actions. The obtained simulation results show that proposed approach provides better results compared with Proportional Integral-Ant Colony Optimization (PI-ACO) controller and adaptive fuzzy model predictive control (AFMPC).

yearjournalcountryeditionlanguage
2014-10-01
10.1109/isic.2014.6967613http://hdl.handle.net/11311/1028776