Speed Tracking Control for Autonomous Electric Vehicle

To achieve the rapidity, accuracy and stability of speed tracking of autonomous electric vehicle under different working conditions, four methods, namely Fuzzy PID, Feedforward PID, Sliding Mode Control and conventional PID, are designed in this paper for comparative analysis. Firstly, a parametric self-tuning Fuzzy PID controller is designed using a combination of fuzzy control and PID control. Secondly, a Feedforward PID controller was designed based on the inverse longitudinal dynamic model of the vehicle and PID control. Thirdly, the sliding mode surface function was designed according to the longitudinal dynamics model of the vehicle and the principle of sliding mode variable structure, and the appropriate sliding film convergence rate was selected to obtain the sliding mode controller. Finally, control simulation software and vehicle dynamics software were used for joint simulation tests and comparisons. The results show that under some common operating conditions, the combined control effect of Fuzzy PID and Feedforward PID is slightly better than that of conventional PID, and the Sliding Mode Control is significantly faster than the other three PID controls in terms of response speed, but it is slightly worse in terms of control accuracy. In some complex working conditions, the comprehensive control effect of Fuzzy PID is significantly better than other control algorithms, which can effectively improve the accuracy and stability of control.