期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 30, 期 10, 页码 4286-4297出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2022.3146986
关键词
Metaheuristics; Cost function; Statistics; Sociology; Tuning; Search problems; Particle swarm optimization; Fuzzy control; optimization; particle filter (PF); particle swarm optimization (PSO); PF-PSO algorithm
资金
- Ministry of Innovation and Technology NRDI Office within the framework of the Autonomous Systems National Laboratory Program [NKFIH-829-2/2021]
- Romanian Ministry of Education and Research
- CNCSUEFISCDI [PN-III-P4-ID-PCE-2020-0269]
- NSERC of Canada
This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The algorithm is applied to the optimal tuning of proportional-integral-fuzzy controllers for position control of integral-type servo systems, resulting in reduced energy consumption. A comparison with other metaheuristic algorithms is provided at the end of the article.
This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF-PSO algorithm consists of two steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning of proportional-integral-fuzzy controllers for the position control of a family of integral-type servo systems is then presented as a second contribution. The reduction in PF-PSO algorithm's cost function allows for reduced energy consumption of the fuzzy control system. A comparison with other metaheuristic algorithms on canonical test functions and experimental results are presented at the end of this article.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据