Robust trajectory tracking control for an underactuated autonomous underwater vehicle based on bioinspired neurodynamics

• Published in: International journal of advanced robotic systems Vol. 15; no. 5; p. 172988141880674
• Main Authors: Jiang, Yunbiao, Guo, Chen, Yu, Haomiao
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.09.2018; Sage Publications Ltd; SAGE Publishing
• Subjects: Adaptive algorithms; Adaptive control; Asymptotic properties; Autonomous underwater vehicles; Computer simulation; Control algorithms; Control stability; Control theory; Coordinate transformations; Disturbances; Dynamic control; Integrals; Robust control; Robustness (mathematics); Sliding mode control; Tracking control; Tracking errors; Trajectory control; Velocity; Underactuated autonomous underwater vehicle; robustness; trajectory tracking control; bioinspired velocity control; adaptive integral sliding mode control
• ISSN: 1729-8814; 1729-8806; 1729-8814
• DOI: 10.1177/1729881418806745

This article investigates the three-dimensional trajectory tracking control problem for an underactuated autonomous underwater vehicle in the presence of parameter perturbations and external disturbances. An adaptive robust controller is proposed based on the velocity control strategy and adaptive integral sliding mode control algorithm. First, the desired velocities are developed using coordinate transformation and the backstepping method, which is the necessary velocities for autonomous underwater vehicle to track the time-varying desired trajectory. The bioinspired neurodynamics is used to smooth the desired velocities, which effectively avoids the jump problem of the velocity and simplifies the derivative calculation. Then, the dynamic control laws are designed based on the adaptive integral sliding mode control to drive the underactuated autonomous underwater vehicle to sail at the desired velocities. At the same time, the auxiliary control laws and the adaptive laws are introduced to eliminate the influence of parameter perturbations and external disturbances, respectively. The stability of the control system is guaranteed by the Lyapunov theorem, which shows that the system is asymptotically stable and all tracking errors are asymptotically convergent. At the end, numerical simulations are carried out to demonstrate the effectiveness and robustness of the proposed controller.