Open AccessComposite learning tracking control for underactuated marine surface vessels with output constraints
Author(s) -
Huaran Yan,
Yingjie Xiao,
Honghang Zhang
Publication year - 2022
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.863
Subject(s) - underactuation , control theory (sociology) , lyapunov function , constraint (computer aided design) , computer science , tracking error , stability (learning theory) , tracking (education) , artificial neural network , lyapunov stability , mathematical optimization , mathematics , control (management) , artificial intelligence , machine learning , nonlinear system , psychology , pedagogy , physics , geometry , quantum mechanics
In this paper, a composite learning control scheme was proposed for underactuated marine surface vessels (MSVs) subject to unknown dynamics, time-varying external disturbances and output constraints. Based on the line-of-sight (LOS) approach, the underactuation problem of the MSVs was addressed. To deal with the problem of output constraint, the barrier Lyapunov function-based method was utilized to ensure that the output error will never violate the constraint. The composite neural networks (NNs) are employed to approximate unknown dynamics. The prediction errors can be obtained using the serial-parallel estimation model (SPEM). Both the prediction errors and the tracking errors were employed to construct the NN weight updating. Using approximation information, the disturbance observers were designed to estimate unknown time-varying disturbances. The stability analysis via the Lyapunov approach indicates that all signals of unmanned marine surface vessels are uniformly ultimate boundedness. The simulation results verify the effectiveness of the proposed control scheme.