Path tracking control based on Tube MPC and time delay motion prediction

Path tracking as an essential function of an autonomous vehicle ensures it tracks the desired path. Its performance could be degraded by wind, road conditions, vehicle parameter uncertainty and signal delay. This study proposes a path tracking control framework for autonomous vehicle by combining tube model predictive control (MPC) and time delay motion prediction (TDMP). Tube MPC with N -step reachable set is proposed to handle disturbances. A robust invariant set is computed efficiently using supporting function and guarantees state constraints in the presence of disturbances. In TDMP, a path during the signal delay is presented and a vehicle kinematics model is adopted to predict changes of vehicle position and yaw. Simulations show that the computational cost with support function for a robust positively invariant set is less than that with Multi-Parametric Toolbox. In addition, the proposed control framework not only addresses disturbances like wind, road conditions and vehicle parameter uncertainty, but also handles the steering signal delay through motion prediction.