6533b857fe1ef96bd12b4274

RESEARCH PRODUCT

An ant colony optimization-based fuzzy predictive control approach for nonlinear processes

Hamid Reza KarimiMohammed ChadliSofiane Bououden

subject

Information Systems and ManagementMeta-optimizationOptimization problemComputer scienceAnt colony optimization algorithmsComputer Science::Neural and Evolutionary ComputationProcess (computing)Computer Science ApplicationsTheoretical Computer ScienceNonlinear systemModel predictive controlArtificial IntelligenceControl and Systems EngineeringControl theoryMetaheuristicSoftware

description

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.

yearjournalcountryeditionlanguage
2015-04-01Information Sciences
https://doi.org/10.1016/j.ins.2014.11.050