Data-driven preview <i>H</i> _∞ control for intelligent suspension systems of vehicle platoons based on multi-player Q-learning model

The integration of high-precision vehicle-mounted visual sensors has revolutionized the field of intelligent suspension systems, enabling the implementation of preview control strategies that significantly enhance ride comfort and safety performance in vehicle platoons. Building on this advancement, this study presents a data-driven preview control framework for active suspension systems tailored to heterogeneous vehicle platoons. The proposed framework leverages a vehicle-to-vehicle (V2V) communication topology to incorporate comprehensive road preview information. Initially, a dynamic model of the active suspension system is formulated, encompassing inter-vehicle-distance preview, wheelbase preview, and look-ahead preview mechanisms. Subsequently, the preview H ∞ control problem is recast as a multi-player zero-sum game to address the interactions within the platoon. To reduce the calibration and design costs associated with the control scheme, a model-free Q-learning algorithm is presented. This approach determines the optimal control policy for the system without requiring prior knowledge of the output matrices, while simultaneously ensuring operational safety and ride comfort. Finally, numerical simulations and comparative analyses are conducted to validate the proposed control scheme. The results indicate that, under various road excitation scenarios, the proposed platoon preview control framework consistently outperforms traditional passive suspension systems and non-preview-based independent control schemes. Furthermore, the performance exhibits a strong correlation with the amount of available preview information. Notably, the scheme achieves optimal control performance in constant-speed platooning scenarios.