Prior-Guided Residual Reinforcement Learning for Active Suspension Control

Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. The approach integrates a Linear Quadratic Regulator (LQR) as a prior controller to ensure baseline stability, while an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm learns the residual control policy to improve adaptability and robustness. Moreover, residual connections and Long Short-Term Memory (LSTM) layers are incorporated into the TD3 structure to enhance dynamic modeling and training stability. The simulation results demonstrate that the proposed method achieves better control performance than passive suspension, a standalone LQR, and conventional TD3 algorithms.