期刊
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
卷 236, 期 9, 页码 1964-1976出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/09544070211054318
关键词
Intelligent vehicles; four-Wheel steering vehicles; model predictive control; LQR; UKF state estimation; optimal control
资金
- National Natural Science Foundation of China [51805009]
The article introduces a new four-wheel active steering control strategy, which utilizes the MPC algorithm for path tracking control, designs an estimator based on UKF theory and low-cost sensors, and combines it with the LQR optimal controller to achieve optimized control of front and rear steering. Simulation results demonstrate that this method performs well in lateral control stability and path tracking accuracy.
Four-wheel steering (4WS) vehicles have better stability control and path following performance than front-wheel steering (FWS) vehicles. Aiming at this characteristic, a new four-wheel active steering control strategy is proposed. For intelligent vehicle path tracking and nonlinear vehicle system state estimation, a path tracking control algorithm based on the MPC algorithm is designed to analyze the stability of the vehicle and set up constraints to achieve accurate tracking of the reference path. Automotive dynamic control systems require information on system variables. For example, the sideslip angle of electric vehicles cannot be measured directly. Based on UKF theory and the information input of low-cost sensors on the vehicle, an estimator is designed to estimate vehicle sideslip angle and yaw rate. According to the difference between the estimated value and the ideal value of the vehicle state, the LQR optimal controller is designed to realize the optimal control of the front and rear steering. And compared with the dynamic simulation results of front-wheel steering, proportional control four-wheel steering and yaw rate feedback four-wheel steering. The experimental results of the simulation platform show that the path tracking 4WS state feedback optimal control method has good lateral control stability and path tracking accuracy.
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