Paper Info
Title
A bioinspired neural dynamics-based approach to tracking control of autonomous surface vehicles subject to unknown ocean currents
Abstract
This paper addresses the trajectory tracking control problem of an autonomous surface vehicle (ASV) subject to unknown ocean currents, where smooth and continuous velocity commands are desirable for safe and effective operation. A novel bioinspired approach is proposed by integrating three neural dynamics models into the conventional Lyapunov synthesis. The tracking controller is derived from the error dynamics analysis of the ASV and the stability analysis of the control system. A simple observer is proposed to estimate the unknown ocean currents, which only requires the position of the ASV. The overall control system under the controller and observer is rigorously proved to be asymptotically stable by a Lyapunov stability theory for cascaded systems. The most contribution is that the proposed tracking controller is capable of eliminating the sharp velocity jumps due to sudden tracking error changes and generating smooth and continuous control signals. In addition, it can deals with the situation with unknown ocean currents. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.
Year
DOI
Venue
2015
10.1007/s00521-015-1839-6
Neural Computing and Applications
Keywords
Field
DocType
Neural dynamics, Tracking control, Autonomous surface vehicles, Unknown ocean currents
Lyapunov function,Control theory,Control theory,Lyapunov stability,Control system,Observer (quantum physics),Trajectory,Mathematics,Tracking error,Stability theory
Journal
Volume
Issue
ISSN
26
8
1433-3058
Citations 
PageRank 
References 
5
0.42
20
Authors
4
Name
Order
Citations
PageRank
Chang-Zhong Pan1252.24
Xuzhi Lai28114.48
Simon X. Yang31029124.34
Min Wu43582272.55