Reinforcement Learning-Based Optimal Control Design for Active Suspension Systems

Vehicle suspension systems play a pivotal role in ensuring both comfort and safety during driving by maintaining stability and handling. Recent advancements in reinforcement learning (RL) for control design offer adaptive, data-driven solutions capable of enhancing efficiency and robustness in managing complex systems. This paper presents the design and application of an RL-based controller for an active suspension system. Leveraging the deep deterministic policy gradients (DDPG) method, our approach showcases significant improvements over conventional controllers. The paper introduces the system model and its adaptation to function effectively within an RL environment. Subsequently, the RL agent is developed, encompassing its observation space, action space, and reward function. Finally, a comprehensive benchmarking study is conducted, comparing the performance of the RL-based controller against other controllers such as the linear-quadratic regulator, full state-feedback controller, and PID controller. This study highlights the strengths and weaknesses of each controller in managing the system and provides insights into potential improvements and tuning strategies. The model and source code for implementing the proposed approach are openly available on GitHub1.¹¹https://github.com/abdohamdy7/suspenstion system control RL