With the increasing demand for ride comfort, safety, and performance in modern vehicles, semiactive suspension systems based on magnetorheological dampers (MRDs) have attracted significant attention because of their fast responses, low energy consumption levels, and controllable damping forces. However, the traditional control strategies often rely on precise mathematical models, making it difficult to handle the inherent nonlinearities and parametric uncertainties of such systems. To address this challenge, a reinforcement learning-based semiactive control framework using proximal policy optimization (PPO) is proposed in this study; the framework enables autonomous control policy optimization through continuous interactions with the environment. First, an MRD model is established based on a phenomenological model with an intelligent optimization algorithm. This model is integrated into a PPO control framework, allowing the agent to generate control currents through environmental interactions, thereby adjusting the output damping force of the damper. The proposed method does not require an explicit system model during training and acquires the optimal control policy through trial and error. The simulation results obtained under stochastic road excitations demonstrate that the designed PPO controller significantly outperforms both the passive and Skyhook controllers in terms of vibration reduction. Moreover, even under 20% parametric uncertainty, the retrained controller maintains good stability and control performance, indicating that the randomized training enhances the generalization ability and adaptability of the learned policy to system parameter variations, thereby providing the potential to develop a truly robust control strategy in future work. This study highlights the potential of reinforcement learning (RL) for use in semiactive suspension control tasks with MRDs and offers a scalable control strategy for complex nonlinear systems.
Jia et al. (Mon,) studied this question.