Shared control for human–machine co-driving vehicles based on constraint-following approach

To ensure the human–machine co-driving vehicle path-tracking accuracy, as well as to diminish the effects of vehicle underactuated characteristics and undesirable driver operation, a parallel shared control framework is proposed in this paper. By structuring a rational driving weights allocation module, the framework enables cooperative driving between the driver and automated system. First, for the front steering vehicle, a robust path-tracking controller is proposed using the constraint-following approach. The system uncertainty is decomposed into matched and non-matched uncertainties. As the non-matched portions are orthogonal to the constraint-following geometric space, they will not affect control performance. Therefore, the constraint-following control (CFC) is designed to handle the path-tracking equality constraint, initial state deviations, and matched uncertainties. Second, a fuzzy rule-based driving weights allocation module is proposed. According to the lateral deviation and the relative accuracy of the steering angle, the allocation module adjusts the driving weight of the driver and automated system to improve the vehicle condition. Finally, the simulation results show that the framework can effectively reduce the emergence caused by the driver or automated system, and significantly improve the path-tracking performance. Additionally, as the controller exhibits sufficient robustness, it can handle multiple sources of uncertainty in vehicle systems.