Robust sliding mode prediction path tracking control for intelligent vehicle

Because of the characteristics of nonlinearity, strong coupling, fast time-varying, and uncertainty of the intelligent vehicle movement, the accurate path tracking control of the intelligent vehicle becomes a challenge. Considering a variety of complex constraints from the control system, this article combines sliding mode control and model prediction control to improve the intelligent vehicle real-time path tracking performance and proposes a robust sliding mode predictive controller for intelligent vehicle path tracking control. The sliding mode predictive controller uses the sliding mode surface in sliding mode control to construct the prediction model and corrects the sliding mode control output through continuous feedback correction and rolling optimization and then obtains the optimal control input which makes sure optimal performance control under system various constraints. The sliding mode predictive controller can simultaneously consider the tracking performance and system complex constraints, which effectively improves the dynamic performance and robustness of the control system. Related simulation results show that sliding mode predictive controller not only solves the problem of system constraints but also improves the accuracy of intelligent vehicle path tracking and the robustness of the system.