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Adaptive Vibration Control of Vehicle Semi-Active Suspension System Based on Ensemble Fuzzy Logic and Reinforcement Learning

The integration of reinforcement learning with fuzzy logic can be effective in compensating the external disturbance and complex dynamic while designing the control strategy for vehicle suspension. The main contribution of this paper is that a learning-based adaptive vibration control strategy is proposed for semi-active suspension system, which combines the fuzzy logic with the reward function of reinforcement learning to improve the robustness and feasibility of the vibration control strategy. What’s more, an improved proximal policy optimization algorithm combined with fuzzy logic is proposed for realizing the trial-and-error reinforcement learning. Specially, the reward function with fuzzy logic is formulated to meet the requirements of suspension performance under different road conditions, in which the fuzzy logic is designed to fuzzily the process the collected road information, real-time update the weight matrix coefficients, and adjust the optimization objectives adaptively. Finally, numerical simulation results are given to prove the effectiveness of the proposed vibration control strategy.