Abstract:

To fully leverage the performance of multi-actuator chassis systems in the path tracking of autonomous vehicles， in this paper the stability analysis method is proposed which considers the influence of actuator characteristics on vehicle dynamics states. Based on this analysis， a model predictive control （MPC） based path tracking controller is designed， with a prediction horizon adaptively adjusted according to stability margins. For autonomous vehicles equipped with active front-wheel steering （AFS） and active rear-wheel steering （ARS）， the vehicle's dynamic state trends under actuator influence are first analyzed in the energy phase plane. A novel stability envelope boundary is defined based on the relationship between the dynamic state variation vector and the front and rear tire force saturation constraints， using Lyapunov's second method. Then， an adaptive prediction horizon calculation method is designed based on stability margin changes during trajectory tracking， and an MPC trajectory tracking controller is constructed using the nonlinear tire model， i.e.， UniTire-Ctrl. The co-simulation results from CarSim and Simulink demonstrate that the proposed stability envelope boundary more accurately estimates the vehicle stability boundary considering actuator characteristics， and the variable horizon MPC path tracking controller effectively balances path tracking accuracy with vehicle lateral stability.

Key words: multi-actuator chassis, MPC, path tracking, extended stability envelope, adaptive predictive horizon