A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

考虑到驾驶员转向特性的不确定性，本文提出了一种人机共享的转向控制，用于跟踪大曲率路径。提出了一种驾驶员－车辆－道路（DVR）模型，其中定义了不确定的特征参数来描述人类在跟踪大曲率路径时的转向行为。然后，使用径向基函数神经网络（RBF）估计不同驾驶员特征的参数并获得这些参数的边界。使用Takagi-Sugeno（TS）模糊逻辑处理DVR模型的驾驶员转向特性和时变车速的参数不确定性。并且在共享转向控制器的设计中考虑了这些参数不确定性。然后基于DVR模型，设计了带有D极分配的TS模糊全阶动态补偿器，以帮助驾驶员转向大曲率的跟踪路径。仿真结果表明，所提出的控制器可以根据驾驶员的熟练程度在道路追随中提供个性化的转向辅助，并且可以显着提高驾驶舒适性。