Adaptive complementary filter-based post-impact control for independently-actuated and differentially-steered autonomous vehicles. (October 2020)

• Record Type: Journal Article
• Authors: Cao, Mingcong; Hu, Chuan; Wang, Jinxiang; Wang, Rongrong; Chen, Nan
• Is Part Of: Mechanical systems and signal processing. Volume 144(2020)
• Publication Date: 2020-10
• Subjects: Autonomous ground vehicles -- State estimation -- Four-wheel independently actuated vehicles -- Post-impact control -- Path planning and control; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106852
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A cascaded ACF scheme is proposed to accurately estimate longitudinal and lateral velocities by considering tire force coupling. Experiments on a scaled FWIA in-wheel-motor-driven EAV have been conducted to verify the effectiveness of the proposed ACF approach. The ACF estimator-based path planning and following control with MPC strategies are originally designed for PIC and for FWIA EAVs. Abstract: This paper investigates a post-impact control (PIC) method for four-wheel independently actuated (FWIA) electric autonomous vehicles (EAVs) after an initial impact. Differential steering angles actuated by differential torques generated from the left and right wheels have been utilized as an inherent redundant control strategy when the steering motor totally fails, to realize the PIC and secondary collision mitigation. To this end, a vehicle state estimation-based PIC strategy is developed in this work, and three contributions have been made as follows: 1) A novel cascaded estimation approach using the adaptive complementary filters (ACF) is proposed to estimate the longitudinal and lateral velocities with low-cost measurement; 2) Experiments on a scaled FWIA EAV with differential steering mechanism have been conducted to verify the proposed ACF approach, indicating that ACF can accurately estimate the longitudinal and lateral velocities; 3) The vehicle velocity estimation-based path planning and following with model predictive control (MPC) strategy are proposed for PIC and for FWIA EAVs, both for the first time. Finally, the verifiable simulation based on the high-fidelity CarSim-Simulink conjoint platform with the experimentally identified vehicle parameters has been conducted, which has verified the proposed ACF-based PIC strategy can effectively avoid the secondary collision and guarantee vehicle stability and control performance.