A new approach to controlling an active suspension system based on reinforcement learning

Abstract

Active suspension provides better vehicle control and safety on the road with optimal driving comfort compared to passive suspension. Achieving this requires a good control system that can adapt to any environment. This article uses a deep reinforcement learning method to develop an optimal neural network that meets the comfort requirements according to ISO 2631-5 standards. The algorithm trains the agent without any prior knowledge of the environment. Various simulations were performed, and the results were validated with the literature and the standard until the appropriate reward function was found. Simple and consistent road profiles were used while maintaining constant system parameters during training. The results show that suspension based on deep reinforcement learning reduces vehicle body acceleration and improves ride comfort without sacrificing suspension deflection and dynamic tire loading. The controller expects the RMS value of the acceleration to be 0.228 with a minimum overrun of the suspended mass.