Twin-Delayed Deep Deterministic Policy Gradient Algorithm for Active Suspensions Incorporating Preview Strategies

In the field of Active Suspension Control (ASC), traditional control algorithms often lack adaptability and require considerable time and effort for parameter tuning. Artificial Intelligence (AI) technologies offer a promising solution to address these limitations. However, most Deep Reinforcement Learning (DRL) algorithms in ASC are based on passive, reactive responses, which will cause the decline in control performance when encountering transient road surface changes. Hence this study proposes a Preview information-based DRL (P-DRL) for ASC. First, the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed to train the neural controller, with a reward function defined according to the system's expected final state. Then, A new preview experience replay buffer is introduced to store system states at future time steps, and it is combined with the existing experience replay buffer to generate coupled predictive control states. Simulation results validate the proposed algorithm, showing a 70.29% improvement in spring acceleration.