Adaptive Neural Control of Underactuated Surface Vessels With Prescribed Performance Guarantees
Document Type
Article
Date of Original Version
12-1-2019
Abstract
This paper presents adaptive neural tracking control of underactuated surface vessels with modeling uncertainties and time-varying external disturbances, where the tracking errors consisting of position and orientation errors are required to keep inside their predefined feasible regions in which the controller singularity problem does not happen. To provide the preselected specifications on the transient and steady-state performances of the tracking errors, the boundary functions of the predefined regions are taken as exponentially decaying functions of time. The unknown external disturbances are estimated by disturbance observers and then are compensated in the feedforward control loop to improve the robustness against the disturbances. Based on the dynamic surface control technique, backstepping procedure, logarithmic barrier functions, and control Lyapunov synthesis, singularity-free controllers are presented to guarantee the satisfaction of predefined performance requirements. In addition to the nominal case when the accurate model of a marine vessel is known a priori, the modeling uncertainties in the form of unknown nonlinear functions are also discussed. Adaptive neural control with the compensations of modeling uncertainties and external disturbances is developed to achieve the boundedness of the signals in the closed-loop system with guaranteed transient and steady-state tracking performances. Simulation results show the performance of the vessel control systems.
Publication Title, e.g., Journal
IEEE Transactions on Neural Networks and Learning Systems
Volume
30
Issue
12
Citation/Publisher Attribution
Dai, Shi Lu, Shude He, Min Wang, and Chengzhi Yuan. "Adaptive Neural Control of Underactuated Surface Vessels With Prescribed Performance Guarantees." IEEE Transactions on Neural Networks and Learning Systems 30, 12 (2019): 3686-3698. doi: 10.1109/TNNLS.2018.2876685.