The invention discloses an active suspension terminal sliding mode control method based on a Q learning penalty function method, and relates to the field of active suspension system control, and the method comprises the steps: firstly, redefining a linear combination of a pitch angle, a roll angle, vertical displacement at a mass center and non-suspension mass vertical motion displacement of a vehicle as system output, and carrying out the feedback linearization through input and output; the system is decomposed into an input and output subsystem and a zero dynamic subsystem; then, for the i < th > agent of the input and output subsystem, a multi-agent-based input and output subsystem terminal sliding mode control method is provided; in order to meet the rapid convergence of the zero-dynamic subsystem, a controller parameter design method based on a Q learning penalty function method is provided, so that the zero-dynamic subsystem is asymptotically stable near a balance point. According to the method, the vertical acceleration, the pitching acceleration and the roll acceleration of the vehicle body are rapidly reduced under different road surface grades, the dynamic performance of the active suspension control system and the computing power requirement of a control algorithm are effectively balanced, and the comfort and the operation stability of the whole vehicle are improved.
基于Q学习罚函数法的主动悬架终端滑模控制方法涉及主动悬架系统控制领域,本发明首先重新定义车辆的俯仰角、侧倾角、质心处垂向位移和非悬架质量垂向运动位移的线性组合为系统输出,通过输入输出反馈线性化,将系统分解为输入输出子系统和零动态子系统;然后针对输入输出子系统的第i个智能体,提出基于多智能体的输入输出子系统终端滑模控制方法;以满足零动态子系统快速收敛为目标,提出基于Q学习罚函数法的控制器参数设计方法,使零动态子系统在平衡点附近渐近稳定。本发明实现了不同路面等级下,车身垂向、俯仰和侧倾加速度的快速降低,有效权衡了主动悬架控制系统的动态性能与控制算法的算力需求,提高了整车的舒适性和操稳性。
Active suspension terminal sliding mode control method based on Q learning penalty function method
基于Q学习罚函数法的主动悬架终端滑模控制方法
20.09.2024
Patent
Elektronische Ressource
Chinesisch
IPC: | B60G VEHICLE SUSPENSION ARRANGEMENTS , Radaufhängungen und Federungen für Fahrzeuge |
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