High-speed autonomous vehicles can significantly enhance performance in obstacle-avoidance trajectory planning and tracking control by integrating existing electronic control systems. Active suspension systems suppress roll motion and maintain balanced wheel load distribution. This keeps the tires in a linear region and allows the controller to achieve enhanced tracking accuracy during aggressive maneuvers. Nevertheless, research related to active suspension systems remains relatively scarce. Nonlinear model predictive control algorithms integrated with active suspension-based tilting techniques have been applied to vehicle obstacle avoidance control by our research group previously, however, limitations existed in ensuring closed-loop stability. For vehicle systems, ensuring safe and stable operation is of paramount importance. To address this issue, this paper proposes Lyapunov contraction constrained nonlinear model predictive control algorithm based on the control Lyapunov function and combines with tilting techniques to design an integrated controller. The proposed controller uses hierarchical control. Specifically, the upper layer generates the reference trajectory using a point-mass vehicle model and model predictive control, while the lower layer performs trajectory tracking based on a nonlinear vehicle model with integrated active steering and tilt control. By incorporating Lyapunov contraction constraints into the NMPC optimization problem, replacing traditional terminal equality constraints, the vehicle trajectory tracking task is achieved while ensuring the stability of the control system. Simulation results, validated through the CarSim/Simulink co-simulation platform, demonstrate the superiority of the proposed controller in multiple scenarios, showing higher precision, providing a new method and practical foundation for the stability design of autonomous vehicle control systems.
Yao et al. (Tue,) studied this question.