A Lyapunov-Based Model Predictive Control for Trajectory Tracking in Harsh Environment

Precise trajectory tracking is the foremost concern to ensure reliable and efficient real-world operations for autonomous vehicles. However, it faces various challenges, including dealing with uncertainties and disturbances in system dynamics and harsh operation environments such as rough terrains and adverse weather conditions. This research proposes a Lyapunov-based model predictive controller to realize accurate trajectory tracking in harsh environment. The path tracking and vehicle dynamic models are first established. In addition, a robust model predictive controller is developed based on the Lyapunov theorem. Specifically, an upper bound of the infinite cost function is derived as matrix inequality forms to guarantee the robustness of the controller. Lastly, the effectiveness of the proposed method is verified through the co-simulation of high-fidelity vehicle model in Carsim and control algorithm in Simulink.