Paper Info
Title
Fractional-order PID controller optimization via improved electromagnetism-like algorithm
Abstract
Based on the electromagnetism-like algorithm, an evolutionary algorithm, improved EM algorithm with genetic algorithm technique (IEMGA), for optimization of fractional-order PID (FOPID) controller is proposed in this article. IEMGA is a population-based meta-heuristic algorithm originated from the electromagnetism theory. It does not require gradient calculations and can automatically converge at a good solution. For FOPID control optimization, IEMGA simulates the ''attraction'' and ''repulsion'' of charged particles by considering each controller parameters as an electrical charge. The neighborhood randomly local search of EM algorithm is improved by using GA and the competitive concept. IEMGA has the advantages of EM and GA in reducing the computation complexity of EM. Finally, several illustration examples are presented to show the performance and effectiveness.
Year
DOI
Venue
2010
10.1016/j.eswa.2010.06.009
Expert Syst. Appl.
Keywords
Field
DocType
controller parameter,competitive concept,electromagnetism-like algorithm,improved electromagnetism-like algorithm,fractional-order pid control,pid control,evolutionary algorithm,genetic algorithm,improved em algorithm,charged particle,fractional-order pid controller optimization,population-based meta-heuristic algorithm,em algorithm,fopid control optimization,genetic algorithm technique,computational complexity,heuristic algorithm,charged particles,local search,pid controller
Population,Control theory,Evolutionary algorithm,PID controller,Control theory,Computer science,Expectation–maximization algorithm,Algorithm,Electromagnetism,Local search (optimization),Genetic algorithm
Journal
Volume
Issue
ISSN
37
12
Expert Systems With Applications
Citations 
PageRank 
References 
27
1.37
13
Authors
2
Name
Order
Citations
PageRank
Ching-Hung Lee159742.31
Fu-Kai Chang2332.17