Sliding mode control for overturning prevention and hardware-in-loop experiment of heavy-duty vehicles based on dynamical load transfer ratio prediction. (March 2022)

• Record Type: Journal Article
• Authors: Lu, Yongjie; Han, Yinfeng; Huang, Weihong; Wang, Yang
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 1(2022)
• Page Start: 68
• Page End: 83
• Publication Date: 2022-03
• Subjects: Heavy vehicles -- multi-body dynamic -- Kalman filtering -- differential braking -- sliding mode control -- rollover prevention control -- hardware-in-loop; Machinery, Dynamics of -- Periodicals; Machine design -- Periodicals; 621.815
• DOI: 10.1177/14644193211057972
• Languages: English
• ISSNs: 1464-4193

Abstract

Aiming at the rollover risk of heavy-duty vehicles, an adaptive rollover prediction and control algorithm based on identification of multiple road adhesion coefficients is proposed, and the control effect has been verified by hardware-in-the-loop experiments. Based on the establishment of a 3 DOFs (Degree of freedom) vehicle dynamic model, the roll angle of the vehicle dynamic model is estimated in real time by using Kalman filter algorithm. In order to ensure the real-time operation of anti-rollover control strategy for multi-body dynamic heavy vehicle model, a sliding mode variable structure controller for anti-rollover of vehicles is designed to determine the optimal yaw moment. Specially, the recognition algorithm of road surface type is integrated into the control rollover algorithm. When the control system with road recognition algorithm recognizes whether the vehicle is in danger of rollover, it can not only adjust the state of the vehicle, but also shorten the time to reach the stable area of the vehicle's lateral load transfer rate by about 2 s. In order to further improve its adaptability and control accuracy, a Hardware-in-loop test platform for three-axis heavy-duty vehicles is built to verify the proposed anti-rollover control strategy. The results prove that the proposed control strategy can accurately predict the rollover risk and control the rollover in time.