Lane keeping of autonomous vehicles based on differential steering with adaptive multivariable super-twisting control. (15th June 2019)

• Record Type: Journal Article
• Authors: Hu, Chuan; Qin, Yechen; Cao, Haotian; Song, Xiaolin; Jiang, Kai; Rath, Jagat Jyoti; Wei, Chongfeng
• Is Part Of: Mechanical systems and signal processing. Volume 125(2019)
• Page Start: 330
• Page End: 346
• Publication Date: 2019-06-15
• Subjects: Autonomous vehicles -- Differential drive assistance steering -- Lane keeping control -- Nonlinear sliding surface -- Adaptive super-twisting algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.09.011
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A novel adaptive multivariable STC approach is proposed to realize lane keeping control with the chattering effect removed, in the presence of the multiple unknown and mismatched disturbances. In the sliding surface, a nonlinear function is designed to adaptively change the damping ratio of the closed-loop system to improve the transient performance. A high-order sliding mode observer is developed to estimate the time derivatives of the lane keeping errors with a vision system to avoid using the information of the lateral velocity. Abstract: This paper investigates the lane keeping control for four-wheel independently actuated autonomous vehicles. To guarantee the vehicle safety when the active-steering motor entirely fails, the steering manoeuvre is realized by the differential drive assisted steering (DDAS) that is generated by the differential moment between the front wheels. A novel adaptive multivariable super-twisting control strategy is proposed to realize the control objective in finite time, considering the multiple unknown and mismatched disturbances of the steering system with the chattering effect removed. In the sliding surface, a nonlinear function is designed to adaptively change the damping ratio of the closed-loop system so as to improve the transient performance of the lane keeping control in the faulty steering condition. The controller design has avoided the use of the lateral velocity which is usually hard to measure in practice. Instead, the lane keeping errors and their time derivatives are estimated with a high-order sliding mode observer based on a vision system. The finite-time convergence of the closed-loop system is proved by the Lyapunov method. Results of CarSim-Simulink simulations with the proposed control strategy compared with a tradition sliding mode controller based on a high-fidelity and full-car model have verified the effectiveness and robustness of the proposed controller in the lane keeping control via DDAS with the guaranteed high performance.