Open AccessSignature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning
Author(s) -
Achmad Komarudin,
Novendra Setyawan,
Leonardo Kamajaya,
Mas Nurul Achmadiah,
Zulfatman Zulfatman
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i1.2667
Subject(s) - particle swarm optimization , inertia , control theory (sociology) , signature (topology) , fuzzy logic , mathematics , mathematical optimization , moment of inertia , linear quadratic regulator , approximation error , fuzzy control system , computer science , optimal control , control (management) , artificial intelligence , physics , geometry , classical mechanics , quantum mechanics
Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.