Hybrid DDPG-LQR control for intelligent active suspension with real-time adaptive fusion

Abstract

Traditional active suspension control faces the challenge of jointly balancing ride comfort, driving safety, and suspension travel utilization. This paper proposes a hybrid control framework that integrates the Deep Deterministic Policy Gradient (DDPG) controller with a Linear Quadratic Regulator (LQR). Using a quarter-car dynamic model, an LQR benchmark is established alongside a DDPG reinforcement learning framework. A novel multi-objective adaptive reward function is designed to co-optimize vertical body acceleration, suspension deflection, and dynamic tire load. Crucially, to bridge these two distinct controllers, we introduce a real-time, performance-based adaptive fusion mechanism that dynamically weights LQR and DDPG to combine stability and adaptability. Simulations demonstrate that the hybrid approach achieves a higher overall performance score than standalone LQR or DDPG, improving over passive suspension by 26.9%, reducing suspension travel by 33.3%, and decreasing energy consumption by 20.7% relative to the LQR baseline, while exhibiting high robustness against extreme perturbations and effective transferability to a full-vehicle environment. Experiments verify the algorithm’s stable force tracking.