Adaptive energy-efficient tracking control of a X rudder AUV with actuator dynamics and rolling restriction. (January 2022)

• Record Type: Journal Article
• Authors: Xia, Yingkai; Xu, Kan; Huang, Zhemin; Wang, Wenjin; Xu, Guohua; Li, Ye
• Is Part Of: Applied ocean research. Volume 118(2022)
• Publication Date: 2022-01
• Subjects: X rudder AUV -- Trajectory tracking -- Improved LOS guidance -- Adaptive chattering-free terminal sliding mode control -- Optimal rudder allocation; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2021.102994
• Languages: English
• ISSNs: 0141-1187

Abstract

Highlights: The influences of complex actuator dynamics are fully considered by optimal X rudder allocation and RBFNN compensator. Adaptive strategies are deeply integrated with the whole controller design process. The proposed controller is energy-efficient and robust against unknown disturbances. The optimal X rudder allocation method can restrict the undesired rolling torque. Trajectory tracking and robust performance of the controller are verified through plenty of comparative simulation cases. Abstract: This paper investigates the problem of trajectory tracking control for a X rudder autonomous underwater vehicle (AUV) subjects to nonlinearities, uncertainties, and complex actuator dynamics. Under an adaptive energy-efficient tracking control scheme, the trajectory tracking problem is decomposed into kinematics and dynamics control. In kinematics control loop, an improved line-of-sight (LOS) guidance law is proposed with fuzzy-based look-ahead distance optimization mechanism, and feedback kinematics control law is designed utilizing Lyapunov method. The dynamics control loop is divided into two subsystems, namely surge tracking and course tracking. Adaptive chattering-free terminal sliding mode control is employed to improve the tracking performance and converging rate, which introduces fuzzy based parameter optimization method to tackle the chattering problem, and robustness to unknown disturbances is addressed by disturbance observers. The influence of complex actuator dynamics is fully considered, where an energy-efficient rudder allocation method is proposed to deal with the multi-objective optimization problem with multi-constraints, including rudder saturations, rolling restriction, etc., and radial-basis-function neural network (RBFNN) based compensator is utilized to deal with the propeller saturation problem. Finally, plenty of comparative numerical simulations are provided to demonstrate the robustness and effectiveness of the proposed approach.