Automated Hyperparameter Tuning for Data-Driven MPC: A Differentiable ADMM Approach for Enhancing Vehicle Stability

For distributed-drive electric vehicles, torque vectoring control based on model predictive control (MPC) has emerged as a preferred strategy to achieve superior performance across diverse driving conditions. However, traditional methods heavily rely on high-order nonlinear models, which require extensive tuning to achieve optimal performance. This article proposes a gradient-based automatic hyperparameter tuning algorithm to enhance vehicle maneuverability and stability by optimizing the MPC objective function. First, a data-driven predictive motion control scheme is introduced, enabling accurate modeling of the real vehicle through a data-driven approach. Subsequently, a differentiable alternating direction method of multipliers (ADMM) is proposed to compute the gradients of state and control input trajectories with respect to MPC weights during optimization. Real vehicle experiments demonstrate that the method effectively captures the characteristics of various driving conditions and automatically adjusts parameters for adaptation. Compared to manual parameter tuning, the strategy improves maneuverability by 33.02%, lateral stability by 27.19%, and ride comfort by 20.78%. Furthermore, in comparison with existing gradient-based tuning methods, the differentiable ADMM achieves more stable online parameter tuning. These metrics are further enhanced by 19.62%, 5.23%, and 34.5%, respectively.