基于粒子群优化的空气悬架自适应反推控制策略

doi:10.13733/j.jcam.issn.2095-5553.2023.10.018

摘要/Abstract

摘要： 为增强半主动空气悬架在非紧急制动下的行驶平顺性以及悬架系统的稳定性，考虑被控空气悬架的参数不确定性与安全约束，提出基于粒子群优化的空气悬架自适应反推控制策略。首先，基于Lyapunov理论推导出空气悬架的虚拟控制律和自适应控制律；其次，基于MATLAB/Simulink搭建半车悬架动力学模型；同时，使用粒子群算法对控制策略的参数进行优化；最后对被动悬架与所提控制策略的空气悬架分别在随机路面和颠簸路面上进行仿真验证。仿真结果表明，在非零初始值下，基于QLF方法的控制器比基于BLF方法的控制器具有更小的增益值，更快的收敛速度，车身垂直加速度均方根改善了98.31%，车身俯仰角加速度均方根改善了97.82%，从而验证QLF控制器的有效性。

关键词: 空气悬架, 粒子群算法, 反步递推控制, 自适应控制

Abstract: In order to improve the ride comfort of the semiactive air suspension under nonemergency braking and the stability of the suspension system, this paper proposed an adaptive backstepping control strategy based on particle swarm optimization in considered of parameter uncertainty and safety constraints of air suspension. Firstly, the virtual control law and adaptive control law of the controlled air suspension were designed by using Lyapunov theory. Secondly, the dynamic model of the semivehicle suspension was built based on MATLAB/Simulink. At the same time, the parameters of the control strategy were optimized by particle swarm optimization. Finally, the passive suspension and the air suspension with the proposed control strategy were simulated and verified on random roads and bump roads. The simulation results showed that under the nonzero initial value, the controller based on the QLF method had a smaller gain value and faster convergence speed than the controller based on the BLF method. The root mean square of vertical acceleration was improved by 98.31%, and the root mean square of body pitch angle acceleration was improved by 97.82%, thus the QLF controller had verified the effectiveness.

Key words: air suspension, particle swarm algorithm, backstepping control, adaptive control

中图分类号:

U463.33

袁春元, 王兴宸, 陶振兴, 朱爱鑫. 基于粒子群优化的空气悬架自适应反推控制策略[J]. 中国农机化学报, 2023, 44(10): 121-128.

Yuan Chunyuan, Wang Xingchen, Tao Zhenxing, Zhu Aixin. Adaptive backstepping control strategy for air suspension based on particle swarm optimization[J]. Journal of Chinese Agricultural Mechanization, 2023, 44(10): 121-128.

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