Neural observer-based path following control for underactuated unmanned surface vessels with input saturation and time-varying disturbance
In this study, a new neural observer-based dynamic surface control scheme is proposed for the path following of underactuated unmanned surface vessels in the presence of input saturation and time-varying external disturbance. The dynamic surface control technique is augmented by a robust adaptive ra...
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| Veröffentlicht in: | International journal of advanced robotic systems Jg. 16; H. 5 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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London, England
SAGE Publications
01.09.2019
Sage Publications Ltd SAGE Publishing |
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| ISSN: | 1729-8806, 1729-8814 |
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| Abstract | In this study, a new neural observer-based dynamic surface control scheme is proposed for the path following of underactuated unmanned surface vessels in the presence of input saturation and time-varying external disturbance. The dynamic surface control technique is augmented by a robust adaptive radial basis function neural network and a nonlinear neural disturbance observer. Radial basis function neural network is employed to deal with system uncertainties, and the nonlinear neural disturbance observer is developed to compensate for the unknown compound disturbance that contains the input saturation approximation error and the external disturbance. Moreover, the stringent known boundary requirement of the unknown disturbance constraint is eliminated with the proposed nonlinear neural disturbance observer. Meanwhile, to deal with the non-smooth saturation nonlinearity, a new parametric hyperbolic tangent function approximation model with arbitrary prescribed precision is constructed, which results in the transient performance improvement for the path following control system. Stability analysis shows that all the signals in the closed-loop system are guaranteed to be ultimately bounded. Comparative simulation results further demonstrate the effectiveness of the proposed control scheme. |
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| AbstractList | In this study, a new neural observer-based dynamic surface control scheme is proposed for the path following of underactuated unmanned surface vessels in the presence of input saturation and time-varying external disturbance. The dynamic surface control technique is augmented by a robust adaptive radial basis function neural network and a nonlinear neural disturbance observer. Radial basis function neural network is employed to deal with system uncertainties, and the nonlinear neural disturbance observer is developed to compensate for the unknown compound disturbance that contains the input saturation approximation error and the external disturbance. Moreover, the stringent known boundary requirement of the unknown disturbance constraint is eliminated with the proposed nonlinear neural disturbance observer. Meanwhile, to deal with the non-smooth saturation nonlinearity, a new parametric hyperbolic tangent function approximation model with arbitrary prescribed precision is constructed, which results in the transient performance improvement for the path following control system. Stability analysis shows that all the signals in the closed-loop system are guaranteed to be ultimately bounded. Comparative simulation results further demonstrate the effectiveness of the proposed control scheme. |
| Author | Qin, Hongde Wang, Jian Wan, Lei Zeng, Jiangfeng Li, Yueming Zhang, Lei |
| Author_xml | – sequence: 1 givenname: Lei surname: Wan fullname: Wan, Lei – sequence: 2 givenname: Jiangfeng orcidid: 0000-0002-4330-2725 surname: Zeng fullname: Zeng, Jiangfeng – sequence: 3 givenname: Yueming surname: Li fullname: Li, Yueming – sequence: 4 givenname: Hongde orcidid: 0000-0002-9794-4491 surname: Qin fullname: Qin, Hongde email: qinhd@hrbeu.edu.cn – sequence: 5 givenname: Lei surname: Zhang fullname: Zhang, Lei – sequence: 6 givenname: Jian surname: Wang fullname: Wang, Jian |
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| Cites_doi | 10.1177/1729881417748127 10.1002/9781119994138 10.1002/rnc.978 10.1109/TAC.2000.880994 10.1016/j.neucom.2017.06.042 10.1109/TIE.2016.2573240 10.1109/TIE.2018.2890500 10.1080/00207721.2015.1056274 10.1016/j.automatica.2008.08.003 10.1109/TAC.2013.2286913 10.1049/iet-cta.2008.0131 10.1109/TII.2018.2877046 10.1016/j.neucom.2015.11.017 10.1109/JOE.2013.2280822 10.1016/j.oceaneng.2006.10.019 10.1109/TNN.2004.839354 10.1109/TCYB.2014.2334695 10.1109/TAC.2011.2122730 10.1007/s40815-017-0401-3 10.1016/j.automatica.2004.01.021 10.1109/9.486648 10.1109/TCYB.2014.2365778 10.1080/00207720902974595 10.1109/TCST.2012.2183676 10.1016/j.oceaneng.2016.06.041 10.1016/j.automatica.2006.04.026 10.1016/S0005-1098(97)00141-6 10.1016/j.automatica.2011.01.025 10.1109/TAC.2007.902731 10.1109/TSMC.2018.2834515 10.1109/TFUZZ.2017.2697399 10.1016/j.oceaneng.2017.08.011 10.1016/j.oceaneng.2019.02.017 10.1109/TIE.2012.2183841 10.1016/j.automatica.2016.11.024 |
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| Keywords | dynamic surface control adaptive control Unmanned surface vessels path following input saturation |
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| SubjectTerms | Adaptive control Approximation Computer simulation Control stability Disturbance observers Feedback control Hyperbolic functions Mathematical analysis Neural networks Nonlinearity Radial basis function Saturation Stability analysis Trajectory planning Transient performance Unmanned vehicles Vessels |
| Title | Neural observer-based path following control for underactuated unmanned surface vessels with input saturation and time-varying disturbance |
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