Research on Parameter Tuning of Electro-Hydrostatic Actuator Position Sliding Mode Controller Based on Enhanced Dynamic Sand Cat Search Optimization Algorithm

Weibo Li (), Shuai Cao, Xiaoqing Deng, Junjie Chen and Hao Zhang
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Weibo Li: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Shuai Cao: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Xiaoqing Deng: Hubei ChuangSiNuo Electrical Technology Corp., Enshi 445000, China
Junjie Chen: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Hao Zhang: School of Automation, Wuhan University of Technology, Wuhan 430070, China

Energies, 2025, vol. 18, issue 8, 1-25

Abstract: This paper proposes an Enhanced Dynamic Sand Cat Search Optimization algorithm (EDSCSO) designed to address the high-order nonlinearities and strong coupling issues in the parameter tuning of the position sliding mode controller for electro-hydrostatic actuators (EHAs). Traditional swarm intelligence optimization algorithms often struggle with the transition from global to local search, which leads to being trapped in local optima and results in lower computational efficiency. To overcome these challenges, the EDSCSO algorithm introduces an escape mechanism, a stochastic elite cooperative bootstrap strategy, and a multi-path differential perturbation strategy. These enhancements significantly increase the diversity of the population, facilitate a smooth transition from global to local search, avoid local optimum traps, and better balance the exploration and exploitation capabilities of the algorithm. Based on this algorithm, the sliding mode surface and convergence rate parameters within the sliding mode controller are optimized. Simulation validations conducted on the combined platform of MATLAB/Simulink and AMESim demonstrate that the sliding mode PID controller optimized by the EDSCSO algorithm achieves smaller steady-state and tracking errors, exhibits greater robustness, and offers enhanced computational efficiency compared to other swarm intelligence optimization algorithms. This study provides an effective optimization strategy to improve the control performance of the EHA position sliding mode controller.

Keywords: electro-hydrostatic actuator (EHA); sliding mode PID; sand cat search optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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