Automated Vehicle Path Following: A Non-Quadratic-Lyapunov-Function-Based Model Reference Adaptive Control Approach With C∞-Smooth Projection Modification

• Published in: IEEE transactions on intelligent transportation systems 2022-11, Vol.23 (11), p.21653-21664
• Main Authors: Zhou, Xingyu, Wang, Zejiang, Wang, Junmin
• Format: Article
• Language: English
• Subjects: Adaptation models; Adaptive control; Automatic control; autonomous driving; Comparative studies; Control methods; Control theory; Controllers; Generators; H∞ control; Kinematics; Machine learning; Mathematical models; Model reference adaptive control; non-quadratic Lyapunov redesign; Parameters; Path tracking; R&D; Research & development; Robustness; smooth parameter projection; Stability analysis; Tracking control; trajectory following; Trajectory planning; Transportation; Transportation systems; Uncertainty; Vehicle dynamics
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3182928

Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced \mathcal {L}^{\mathbf {1+\alpha }} tracking performance. Second, a \mathcal{C}^{ \boldsymbol {\infty }} -differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.