Active Suspension Performance Enhancement Based on Improved DDPG Algorithms

Reinforcement learning (RL) has gained significant attention due to its end-to-end learning capabilities and model-free nature. In the realm of vehicle engineering, active suspensions are crucial for enhancing both comfort and safety. The Deep Deterministic Policy Gradient (DDPG) algorithm, characterized by its stability and efficiency, offers valuable guidance for addressing the nonlinear issues in suspension systems. However, its learning efficiency may be compromised when dealing with high-dimensional state or action spaces. Therefore, this research primarily focuses on improving the DDPG algorithm by implementing enhancements by improved reward modules and integrating Long Short-Term Memory (LSTM), in order to maximize its control performance. By conducting five sets of experiment, the results demonstrate that the DDPG algorithm, after being integrated with various methods, exhibits superior control performance.