Path-tracking control based on adaptive tube-based robust MPC for high-speed intelligent vehicles

To improve path-tracking performance of high-speed intelligent vehicles, this paper proposes a path-tracking control design method based on an adaptive tube-based robust model predictive control (A-TRMPC) controller. Built upon a tube-based robust model predictive control (Tube-RMPC) controller with linear quadratic regulator (LQR) feedback control, the A-TRMPC is designed by incorporating a variable look-ahead preview distance inspired by human-driver driving and considering variable prediction and control horizon. The A-TRMPC exhibits the distinguished features: (1) adapting different operating conditions by varying the prediction horizon, control horizon, and the look-ahead distance; (2) tightening constraints by introducing a robust positive invariant set to improve the robustness of the controller against prediction vehicle model errors and external uncertainty disturbances. Considering the influence of prediction horizon, control horizon and look-ahead distance on the control effect, these parameters are treated as design variables, which are optimized offline using a quantum particle swarm optimization (QPSO) search algorithm with path-tracking accuracy and vehicle stability as design criteria. Finally, the effectiveness of the proposed path-tracking control design method is evaluated with a benchmark comparison using CarSim–Matlab/Simulink-based co-simulation.