Time: 2023-03-10 | Counts: |
HU W Q, YANG X J, DONG Y Q,et al.Research on parameter optimization method of electro-hydraulic actuator controller based on particle swarm algorithm[J].Journal of Henan Polytechnic University(Natural Science) ,2023,42(2):114-119.
doi:10.16186/j.cnki.1673-9787.2021050130
Received:2021/05/29
Revised:2021/11/18
Published:2023/03/25
Research on parameter optimization method of electro-hydraulic actuator controller based on particle swarm algorithm
HU Wanqiang1, YANG Xijun2, DONG Yongqiang1, LI Yaohui1
1.School of Electrical & Mechanical Engineering,Xuchang University,Xuchang 461000,Henan,China;2.School of Mechanical and Power Engineering,Yingkou Institute of Technology,Yingkou 115014,Liaoning,China
Abstract:In order to solve the problems of nonlinear,uncertain and chattering in electro-hydraulic actuator system(EHA),a particle swarm optimization(PSO)algorithm was proposed to optimize the parameters of the EHA controller,the traditional sliding mode control(SMC) variables were optimized by PSO algorithm in real time under the condition of external interference,so as to make the EHA system produce accurate motion.Firstly,the EHA was modeled,the system state equation was obtained,then the traditional SMC was introduced,the PSO algorithm was introduced into the SMC to optimize its switching control in real time,the optimal SMC variable was optimized in case of external interference,so as to ensure that the EHA system could produce accurate position tracking in the light of the input signal.The system model of sliding mode controller was established and simulated by using Simulink module of MATLAB.the simulation results showed that the proposed controller could make the sliding mode variables converge quickly under the condition of disturbance,and could realize the fast and accurate position tracking of the target curve,which greatly improved the robustness of the system,thus the results showed that the proposed control scheme was effective and scientific.
Key words:EHA system;PSO algorithm;SMC;real-time optimization;position tracking