Data-driven <i>H</i> ₂ / <i>H</i> _∞ control for full-car active suspension systems via stochastic reinforcement learning algorithm

Under complex and uneven road conditions, variations in vehicle load can readily induce stochastic disturbances in key parameters such as the center of mass and moments of inertia. These numerous and unpredictable fluctuations introduce stochastic structural uncertainties into the full-vehicle dynamic model, potentially destabilizing conventional suspension control strategies and degrading their overall performance. To address these challenges, this paper investigates a data-driven reinforcement learning-based H 2 / H ∞ vibration control strategy, aiming to achieve robust suspension control in the presence of stochastic structural uncertainties. Firstly, a control-oriented model of the full-vehicle active suspension system is constructed. The control problem is reformulated as a stochastic zero-sum game problem, and an iterative algorithm is given to solve the associated stochastic game algebraic Riccati equation. To alleviate the complexity and high cost of parameter tuning and model identification, a data-driven off-policy reinforcement learning algorithm is introduced. This approach effectively compensates for the adverse effects introduced by stochastic structural uncertainties and provides a robust control solution without requiring explicit knowledge of system parameters. Finally, numerical simulations are conducted to validate the robustness of the proposed approach. The results show that the proposed algorithm can converge to the optimal solution within 10 steps.