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An adaptive chaos particle swarm optimization for tuning parameters of PID controller
Author(s) -
Nie ShanKun,
Wang YuJia,
Xiao Shanli,
Liu Zhifeng
Publication year - 2017
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2314
Subject(s) - pid controller , particle swarm optimization , control theory (sociology) , benchmark (surveying) , maxima and minima , chaotic , convergence (economics) , controller (irrigation) , multi swarm optimization , computer science , chaos (operating system) , premature convergence , mathematical optimization , mathematics , engineering , control engineering , artificial intelligence , temperature control , control (management) , agronomy , geodesy , computer security , economic growth , economics , biology , geography , mathematical analysis
Summary An adaptive chaos particle swarm optimization (ACPSO) is presented in this paper to tune the parameters of proportional‐integral‐derivative (PID) controller. To avoid the local minima, we introduced a constriction factor. Meanwhile, the chaotic searching is combined with the particle swarm optimization to improve the ability of the proposed algorithm. A series of experiment is performed on 6 benchmark functions to confirm its performance. It is found that the ACPSO can get better solution quality in solving the global optimization problems and avoiding the premature convergence. Based on it, the proposed algorithm is applied to tune the PID controller's parameters. The performances of the ACPSO are compared with different inspired algorithms, and these results show that the ACPSO is more robust and efficient when it is used to find the optimal parameters of PID controller.