Fuzzy-PID controller design for RGV speed track based on improved PSO algorithm

• Published in: Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 46; no. 11
• Main Authors: He, Changjiang, Zhou, Deqiang, Sheng, Weifeng, Xu, Mingrui, Xi, Qing, Chen, Quyan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-11-2024; Springer Nature B.V
• Subjects: Algorithms; Control systems design; Controllers; Design optimization; Engineering; Error reduction; Fuzzy control; Machine learning; Mechanical Engineering; Parameter identification; Particle swarm optimization; Proportional integral derivative; System identification; Technical Paper; Tracking problem; Transfer functions; RGV; Optimization algorithms; PSO; Fuzzy-PID
• ISSN: 1678-5878; 1806-3691
• DOI: 10.1007/s40430-024-05210-4

In response to the RGV speed tracking problem, a multi-strategy enhanced particle swarm optimization (PSO) algorithm was proposed to optimize the Fuzzy-PID controller. Firstly, the operation of the RGV was modeled, and the transfer function was obtained using the system identification toolbox. Then, by integrating ICMIC mapping, adaptive parameters, Levy flights, and dynamic feedback learning strategies, the PSO algorithm was improved and the Fuzzy-PID controller was optimized. Simulation results show that compared to PID, PSO-PID, and Fuzzy-PID, the ILR-PSO-Fuzzy-PID effectively reduces the sum of squared errors (SSE) and average peak error, exhibiting better dynamic performance.