摘要
针对具有连续可调阻尼(Continuous Damping Control,CDC)减振器的半主动悬架智能控制问题,提出基于“双延时深度确定性策略梯度”(Twin Delayed Deep Deterministic Policy Gradient,简称双延时DDPG)的半主动悬架控制策略。首先构建四自由度二分之一半主动悬架系统仿真模型,然后搭建了CDC减振器正、逆模型,通过创建基于双延时DDPG算法的强化学习训练环境,在MATLAB/Simulink环境下分别开展两种典型工况,即典型随机路面与减速带路面下的半主动悬架系统控制效果仿真实验,与被动悬架对比,基于双延时DDPG强化学习控制算法的半主动悬架的簧上质量垂向加速度均方根值分别降低17.69%、33.42%,车身俯仰角加速度均方根值分别降低8.67%、8.27%,基于双延时DDPG控制策略使半主动悬架系统获得了更佳平顺性。
This paper proposes a semi-active suspension control strategy based on the Twin Delayed Deep Deterministic Policy Gradient(DDPG)for the intelligent control problem of semi-active suspension with Continuous Damping Control(CDC)dampers.Firstly,a simulation model of a four-degree-of-freedom half active suspension system was constructed.Then,the forward and inverse models of the CDC damper were constructed.By creating a reinforcement learning training environment based on the double delay DDPG algorithm,two typical working conditions were carried out in MATLAB/Simulink environment,namely,semi-active suspension system control effect simulation experiments under typical random road surfaces and deceleration belt road surfaces,and compared with passive suspension,The root mean square values of the vertical acceleration of the spring mass in the semi-active suspension based on the double delay DDPG reinforcement learning control algorithm were reduced by 17.69%and 33.42%,respectively.The root mean square values of the vehicle pitch angle acceleration were reduced by 8.67%and 8.27%,respectively.The double delay DDPG control strategy enables the semi-active suspension system to achieve better smoothness.
作者
魏文智
谢琪琦
孙京哲
严天一
WEI Wen-zhi;XIE Qi-qi;SUN Jing-zhe;YAN Tian-yi(School of Mechanical and Electrical Engineering,Qingdao University,Qingdao 266071)
出处
《制造业自动化》
2025年第6期85-92,共8页
Manufacturing Automation
基金
国家自然科学基金(51475248)
山东省自然科学基金面上项目(ZR2016EEM49)。
关键词
半主动悬架
双延时DDPG
强化学习
连续可调阻尼
semi-active suspension
twin delayed DDPG
reinforcement learning
continuous damping control
