Data-Driven Lyapunov-Based Model Predictive Control for Improved Trajectory Tracking in Multi-Wheel-Independent-Drive Electric Vehicle

Abstract

This paper proposes a data-driven Lyapunov-based Model Predictive Control (LMPC) method for multi-wheelindependent-drive electric vehicles to enhance the trajectory tracking accuracy while ensuring the vehicle stability. To improve the accuracy of the vehicle dynamics model, we first develop a temporal residual network to learn the residual between the nominal vehicle dynamics and the actual vehicle dynamics from a lot of training data offline. The temporal residual network predicts the vehicle dynamics residual online based on the vehicle states within a past time window. Then, by combining the nominal vehicle dynamics model with the temporal residual network, a more accurate compensation model is obtained. Building on this, we propose a novel data-driven control strategy specifically optimized for trajectory tracking. To ensure vehicle stability, a Lyapunov-based constraint based on the designed backstepping controller is incorporated into the data-driven LMPC. Subsequently, theoretical analysis is presented to validate the stability of the system. In the Carsim & Simulink co-simulation environment, we validated the effectiveness of the proposed temporal residual network and tracking control algorithm through open-loop and closed-loop simulations.