To address the challenges of strong nonlinearity and uncertain disturbances in electromagnetic suspension systems, this paper proposes an RL-Fuzzy adaptive control architecture that integrates reinforcement learning with fuzzy logic.The core innovation involves utilising fuzzy rules to dynamically adjust the exploration rate of the deep deterministic policy gradient algorithm and incorporating Lyapunov stability constraints to suppress current overshoots.Validated using the Springer Nature real vehicle bench dataset, the proposed method reduces the root mean square of body acceleration to 1.15 m/s 2 (surpassing the ISO 2631 high-comfort threshold) and full optimisation of key indicators: safety (tyre displacement variance 1.89 mm 2 ), energy consumption (current rms 1.28 A 2 ), training efficiency (42.3% reduction in steps).This approach provides a computationally efficient robust control framework for intelligent suspension systems.
Ding et al. (Thu,) studied this question.