Trajectory Tracking for Autonomous Ground Vehicles with Model Uncertainty

In this work, an adaptive robust model predictive control scheme is proposed for the trajectory tracking of autonomous ground vehicles. Aiming at the existence of uncertain parameters in the vehicle kinematics model, a set member identification algorithm based on the kinematic error equation is designed. Combined with the error constraint term of the model prediction algorithm, the uncertain parameters of the model are identified in the process of trajectory tracking control. While maintaining the robustness of the control algorithm, it has certain adaptive characteristics. Compared with the nonlinear robust model prediction algorithm used in trajectory tracking control under the same condition, the control algorithm in this paper has higher tracking accuracy.