Adaptive trajectory tracking control strategy of intelligent vehicle. (May 2020)

• Record Type: Journal Article
• Authors: Zhang, Shuo; Zhao, Xuan; Zhu, Guohua; Shi, Peilong; Hao, Yue; Kong, Lingchen
• Is Part Of: International journal of distributed sensor networks. Volume 16:Number 5(2020)
• Publication Date: 2020-05
• Subjects: Trajectory tracking -- adaptive sliding mode control algorithm -- coordination control -- dynamic state -- intelligent vehicle; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147720916988
• Languages: English
• ISSNs: 1550-1329

Abstract

The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.