The suspension system is a crucial part of the automotive chassis, improving vehicle ride comfort and isolating passengers from rough road excitation. Unlike passive suspension, which has constant spring and damping coefficients, active suspension incorporates electronic actuators into the system to dynamically control stiffness and damping variables. However, effectively controlling the suspension system poses a challenging task that necessitates real-time adaptability to various road conditions. This paper presents the Physics-Guided Deep Reinforcement Learning (DRL) for adjusting an active suspension system's variable kinematics and compliance properties for a quarter-car model in real time. Specifically, the outputs of the model are defined as actuator stiffness and damping control, which are bound within physically realistic ranges to maintain the system's physical compliance. The proposed model was trained on stochastic road profiles according to ISO 8608 standards to optimize the actuator's control policy. According to qualitative results on simulations, the vehicle body reacts smoothly to various novel real-world road conditions, having a much lower degree of oscillation. These observations mean a higher level of passenger comfort and better vehicle stability. Quantitatively, DRL outperforms passive systems in reducing the average vehicle body velocity and acceleration by 43.58% and 17.22%, respectively, minimizing the vertical movement impacts on the passengers. The code is publicly available at github.com/anh-nn01/RL4Suspension-ICMLA23.

翻译：悬架系统是汽车底盘的关键组成部分，用于提升车辆行驶平顺性并隔离乘客与崎岖路面的激励。与具有恒定弹簧和阻尼系数的被动悬架不同，主动悬架在系统中集成了电子执行器，以动态控制刚度和阻尼变量。然而，有效控制悬架系统是一项具有挑战性的任务，需要实时适应各种道路条件。本文提出物理引导深度强化学习（DRL）方法，用于实时调整四分之一车辆模型的主动悬架系统可变运动学与柔顺特性。具体而言，模型输出被定义为执行器刚度和阻尼控制，其数值被限制在物理合理的范围内以保持系统的物理一致性。所提模型依据ISO 8608标准在随机路面轮廓数据上进行训练，以优化执行器的控制策略。仿真定性结果表明，车辆车身对各种新颖真实道路条件响应平顺，振荡幅度显著降低。这些观察意味着更高水平的乘客舒适性与更优的车辆稳定性。定量分析显示，DRL在降低平均车身速度与加速度方面分别优于被动系统43.58%和17.22%，从而最小化垂直运动对乘客的影响。代码已公开于github.com/anh-nn01/RL4Suspension-ICMLA23。