A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

The rapid development of the active front-wheel steering technique makes it possible to pursue precise, fast, and reliable path-tracking control for autonomous vehicles. To mitigate the impact of time-varying model parameters and system disturbances on the control performance, this article proposes a continuous-time solving scheme for the autonomous path- tracking problem, which combines the robust model predictive control (RMPC) technique, non-parallel distributed compensation (non-PDC) method, and polytopic model. First, considering the time-varying longitudinal vehicle velocity, a polytopic model is utilized to construct a vehicle path-tracking system with a simpler structure. Then, a state-feedback controller for the continuous system model is developed based on the non- PDC method. By using the Lyapunov stability proof to transform the optimal control problem into the linear matrix inequalities (LMIs) solving form, an RMPC method is used to ensure path-tracking accuracy. Moreover, a robust compensation control is introduced to improve the anti-disturbance and transient response of the system. The virtual simulation by Carsim/ Simulink and field vehicle tests are conducted to assess the proposed controller. Both the simulation and field vehicle tests prove the effectiveness of the proposed controller in improving the path-tracking performance.