Closed-loop layered control strategy of front wheel angle for automatic parking system
(1. Automotive Engineering Research Institute, Jiangsu University， Zhenjiang, Jiangsu 212013， China; 2. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013， China; 3. School of Computer Science and Communication Engineering, Jiangsu University， Zhenjiang, Jiangsu 212013， China)
Abstract：To improve the anti-disturbance capability of controller in automatic parking system and optimize the execution effect of control law, the closed-loop layered controller of front wheel angle was designed. The non-smooth control law based on the fal function was designed with driving distance as non-time reference to output the target front wheel angle. The front wheel angle observer was designed based on the Ackerman steering model, and the fuzzy sliding mode controller was used to implement closed-loop lateral control. The Carsim/Simulink co-simulation was built to verify the feasibility, tracking effect and robustness of the designed controller by simulating typical parking scenarios. A real vehicle test platform was used for testing. The results show that the closed-loop layered controller of front wheel angle can quickly and accurately track the target path, improve the lateral control accuracy of the parking system and ensure the tracking control effect under unknown steering nonlinear disturbances.
GUO K H, LI H, SONG X L, et al. Study on path tracking control strategy of automatic parking system[J]. China Journal of Highway and Transport, 2015,28(9):106-114.（in Chinese）
［2］
YE H, JIANG H B, MA S D, et al. Linear model predictive control of automatic parking path tracking with soft constraints[J].International Journal of Advanced Robotic Systems, DOI:10.1177/1729881419852201.
［3］
SHIN J H, JUN H B, KIM J G. Dynamic control of intelligent parking guidance using neural network predictive control[J].Computers & Industrial Engineering, 2018,120:15-30.
［4］
LI M Y, ZHOU P, HE X M, et al. Parallel parking path planning and tracking control based on adaptive algorithms[J]. International Journal of Automotive Technology, 2021,22(4):949-965.
［5］
ZHANG J R, CHEN H, SONG S Y, et al. Reinforcement learning-based motion planning for automatic parking system[J].IEEE Access, 2020,8:154485-154501.
HE L, LI F L, WEI Y J, et al. Backstepping control algorithm for steer-by-wire angle tracking [J]. China Journal of Highway and Transport, 2021,34(9):51-60.（in Chinese）
［9］
ZHANG H, WANG J M. Active steering actuator fault detection for an automatically-steered electricground vehicle[J]. IEEE Transactions on Vehicular Technology, 2017,66(5):3685-3702.
CHEN T, XU X, CAI Y F, et al. Coordinated control of front-wheel steering angle and yaw stability for unmanned ground vehicle based on state estimation[J].Transactions of Beijing Institute of Technology, 2021,41(10):1050-1057.（in Chinese）
JIANG H B, LI C X, MA S D, et al. Path tracking control of automatic parking for intelligent vehicle based on non-smooth control strategy[J]. Journal of Jiangsu University (Natural Science Edition), 2017,38(5):497-502.（in Chinese）
HUANG Y, HAN J Q. Analysis and design of nonlinear continuous second-order extended state observer[J].Science Bulletin, 2000,45(13):1373-1379.（in Chinese）
［14］
GAO Z Q. Scaling and bandwidth-parameterization based controller tuning[C]∥Proceedings of the IEEE 2003 American Control Conference. Piscataway，USA:IEEE, 2003:4989-4996.
