Paper Info
Title
An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics
Abstract
This paper proposes an efficient neural network (NN) approach to tracking control of an autonomous surface vehicle (ASV) with completely unknown vehicle dynamics and subject to significant uncertainties. The proposed NN has a single-layer structure by utilising the vehicle regressor dynamics that expresses the highly nonlinear dynamics in terms of the known and unknown dynamic parameters. The learning algorithm of the NN is simple yet computationally efficient. It is derived from Lyapunov stability analysis, which guarantees that all the error signals in the control system are uniformly ultimately bounded (UUB). The proposed NN approach can force the ASV to track the desired trajectory with good control performance through the on-line learning of the NN without any off-line learning procedures. In addition, the proposed controller is capable of compensating bounded unknown disturbances. The effectiveness and efficiency are demonstrated by simulation and comparison studies.
Year
DOI
Venue
2013
10.1016/j.eswa.2012.09.008
Expert Syst. Appl.
Keywords
Field
DocType
autonomous surface vehicle,bounded unknown disturbance,off-line learning procedure,control system,proposed controller,unknown dynamic parameter,proposed nn approach,efficient neural network approach,good control performance,proposed nn,on-line learning,robots,neural networks
Control theory,Nonlinear system,Control theory,Computer science,Lyapunov stability,Vehicle dynamics,Control system,Artificial neural network,Trajectory,Bounded function
Journal
Volume
Issue
ISSN
40
5
0957-4174
Citations 
PageRank 
References 
15
0.64
12
Authors
4
Name
Order
Citations
PageRank
Chang-Zhong Pan1252.24
Xuzhi Lai28114.48
Simon X. Yang31029124.34
Min Wu43582272.55