Optimal Control Method of Path Tracking for Four-Wheel Steering Vehicles

Path tracking is a key technique for intelligent electric vehicles, while four-wheel steering (4WS) technology is of great significance to improve its accuracy and flexibility. However, the control methods commonly used in path tracking for a 4WS vehicle cannot take full advantage of the additional steering freedom of the 4WS vehicle, because of restricting the relationship between the front and rear wheels steering angle. To address this issue, we derive a kinematic model without the restriction based on the small-angle assumption. Then, the objective function and constraints of system control quantity optimization are designed based on the tracking error model. After the optimization problem is solved in the form of quadratic programming with constraints, the control sequence with the smallest performance index is obtained through rolling optimization. The proposed method is tested on a high-fidelity Carsim/Simulink co-simulation platform and an experimental vehicle. The results show that the standard deviation of the lateral error and the yaw angle error of the algorithm is less than 0.1 m and 3.0 degrees, respectively. Compared with the other two algorithms, the control of the front and rear wheels angle of this method is more flexible and the tracking accuracy is higher.

Automated summary

Path tracking is crucial for intelligent electric vehicles, and four-wheel steering (4WS) technology plays a significant role in improving accuracy and flexibility. However, the commonly used control methods for 4WS vehicles in path tracking do not fully utilize the additional steering freedom. To address this issue, a kinematic model without restrictions is derived, and the optimization of system control quantity is achieved through solving an optimization problem with constraints.