关键词: 模糊逻辑；驾驶员模型；控制理论

Abstract: Fuzzy logic intelligent control is widely used in the control field because it does not require precise mathematical models and can be better applied to nonlinear systems. The driver model mainly analyzes the effect of vehicle path tracking. In order to improve the accuracy and adaptability of vehicle path tracking, a human-vehicle-road closed-loop system needs to be established. Because the vehicle model is highly non-linear, it cannot be established with precise mathematical expressions. At the same time, because the driver has lag factors in response and operation, it is difficult to establish a human-vehicle-road closed-loop system model with an accurate mathematical model. The driver model's decision-making on the steering wheel is mainly processed and analyzed by introducing control theory. Therefore, this paper applies fuzzy logic intelligent control to the input control of the steering wheel angle. Through fuzzy logic intelligent control, a two-dimensional input variable of lateral error and vehicle speed is established, and a steering wheel angle is a one-dimensional output variable control model. The steering wheel angle of the vehicle is determined by four steps: fuzzification, membership function selection, fuzzy rule formulation, and defuzzification to determine the optimal steering wheel angle. Avoid the dependence on the mathematical expression of the vehicle model. Through simulation verification, the established driver model can be better adapted to the nonlinear system and can effectively improve the accuracy of vehicle path tracking.

Key words: Fuzzy logic; Driver model; Control theory