Using AI to control the car’s active suspension system

Abstract

This paper presents the Physics-Guided Deep Re- inforcement Learning (DRL) for adjusting an active suspension system’s variable kinematics and compliance properties for a quarter-car model in real-time. The proposed model was trained on stochastic road profiles according to ISO 8608 standards to optimize the actuator’s control policy. The forces generated from the actuator for stiffness and damping control are bound within specific ranges to maintain the system’s physical consistency. 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.