Vehicle ride comfort optimization in the post-braking phase using residual reinforcement learning

Owing to increasing urban congestion, ensuring vehicle ride comfort during the post-braking phase has become an essential requirement. However, achieving vehicle ride comfort using current conventional methods is challenging due to the vehicles' complex dynamics. This paper proposes a novel controller with residual reinforcement learning, combining the advantages of the model-free reinforcement learning algorithm, heuristic optimization algorithm, and prior expert knowledge to significantly improve training efficiency. The nonlinear and transient characteristics of the tire and vehicle are modeled to improve the control accuracy. On-vehicle experiments are performed using a skateboard chassis. The experimental results show that the proposed strategy achieves significant improvement in vehicle ride comfort under various braking scenarios. We believe that this technology has the potential to alleviate vehicle discomfort issues in daily life.