Self-Constructing Adaptive Robust Fuzzy Neural Tracking Control of Surface Vehicles With Uncertainties and Unknown Disturbances

In this paper, a novel self-constructing adaptive robust fuzzy neural control (SARFNC) scheme for tracking surface vehicles, whereby a self-constructing fuzzy neural network (SCFNN) is employed to approximate system uncertainties and unknown disturbances, is proposed. The salient features of the SAR...

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Vydané v:	IEEE transactions on control systems technology Ročník 23; číslo 3; s. 991 - 1002
Hlavní autori:	Wang, Ning, Joo Er, Meng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.05.2015
Predmet:	Adaptation models Adaptive robust tracking control Approximation methods Robustness Sea surface self-constructing fuzzy neural network (SCFNN) surface vehicle Uncertainty Vehicle dynamics Vehicles self-constructing fuzzy neural network (SCFNN) Adaptive robust tracking control surface vehicle
ISSN:	1063-6536, 1558-0865
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Abstract	In this paper, a novel self-constructing adaptive robust fuzzy neural control (SARFNC) scheme for tracking surface vehicles, whereby a self-constructing fuzzy neural network (SCFNN) is employed to approximate system uncertainties and unknown disturbances, is proposed. The salient features of the SARFNC scheme are as follows: 1) unlike the predefined-structure approaches, the SCFNN is able to online self-construct dynamic-structure fuzzy neural approximator by generating and pruning fuzzy rules, and achieve accurate approximation; 2) an adaptive approximation-based controller (AAC) is designed by combining sliding-mode control with SCFNN approximation using improved projection-based adaptive laws, which avoid parameter drift and singularity in membership functions simultaneously; 3) to compensate for approximation errors, a robust supervisory controller (RSC) is presented to enhance the robustness of the overall SARFNC control system; and 4) the SARFNC consisting of AAC and RSC can achieve an excellent tracking performance, whereby tracking errors and their first derivatives are globally uniformly ultimately bounded. Simulation studies and comprehensive comparisons with traditional adaptive control schemes demonstrate remarkable performance and superiority of the SARFNC scheme in terms of tracking errors and online approximation.
AbstractList	In this paper, a novel self-constructing adaptive robust fuzzy neural control (SARFNC) scheme for tracking surface vehicles, whereby a self-constructing fuzzy neural network (SCFNN) is employed to approximate system uncertainties and unknown disturbances, is proposed. The salient features of the SARFNC scheme are as follows: 1) unlike the predefined-structure approaches, the SCFNN is able to online self-construct dynamic-structure fuzzy neural approximator by generating and pruning fuzzy rules, and achieve accurate approximation; 2) an adaptive approximation-based controller (AAC) is designed by combining sliding-mode control with SCFNN approximation using improved projection-based adaptive laws, which avoid parameter drift and singularity in membership functions simultaneously; 3) to compensate for approximation errors, a robust supervisory controller (RSC) is presented to enhance the robustness of the overall SARFNC control system; and 4) the SARFNC consisting of AAC and RSC can achieve an excellent tracking performance, whereby tracking errors and their first derivatives are globally uniformly ultimately bounded. Simulation studies and comprehensive comparisons with traditional adaptive control schemes demonstrate remarkable performance and superiority of the SARFNC scheme in terms of tracking errors and online approximation.
Author	Ning Wang Meng Joo Er
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Cites_doi	10.1109/TNN.2008.2003290 10.1109/TNNLS.2013.2292704 10.1049/iet-cta.2011.0176 10.1109/TSMCB.2012.2196039 10.1109/TNN.2010.2042611 10.1109/3477.537311 10.1142/S0129065710002486 10.1016/S0967-0661(02)00009-6 10.1109/TFUZZ.2003.819837 10.1109/TSMCA.2009.2030164 10.1007/0-8176-4470-9_23 10.1109/ACC.2009.5160064 10.1109/48.380245 10.1109/TNNLS.2012.2225845 10.1109/TNN.2011.2159865 10.1109/TIE.2008.926778 10.1109/TSMCB.2004.824525 10.1109/87.572132 10.1109/TIE.2008.2005933 10.1109/TNN.2007.899178 10.1016/j.neucom.2013.01.062 10.1109/TFUZZ.2004.825062 10.1049/ip-cta:20040902 10.1109/TSMCB.2011.2108283 10.1016/j.neucom.2009.05.006 10.1109/TCST.2002.806465 10.1016/j.automatica.2009.03.010 10.1109/TFUZZ.2003.814845 10.1016/S0005-1098(01)00199-6 10.1109/87.852916 10.1109/37.248001 10.1007/s11063-011-9181-1 10.1049/ip-cta:19971032 10.1109/TFUZZ.2011.2136349 10.1109/TIE.2013.2258299 10.1109/72.977306 10.1109/TFUZZ.2003.814833 10.1109/TIE.2007.893056 10.1016/j.automatica.2004.10.006 10.1109/TCST.2006.872507 10.1109/TII.2012.2205584 10.1109/TFUZZ.2003.812694 10.1109/TSMCB.2003.808177 10.1109/TFUZZ.2009.2038277 10.1002/0471781819 10.1007/978-1-84882-730-1 10.1109/TFUZZ.2011.2144600 10.1109/TSMCB.2002.1018767 10.1109/TNN.2004.841786 10.1016/j.fss.2004.07.007 10.1109/TCST.2010.2045654 10.1016/j.automatica.2010.02.024
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References	ref57 ref13 ref12 slotine (ref4) 1991 ref15 li (ref20) 2010; 18 liu (ref25) 2011; 22 ref14 ref53 ref52 ref55 ref11 ref54 ref10 ref17 ref16 ref19 ref51 ref50 ref46 wang (ref56) 2013; 15 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 xu (ref27) 2014; 25 ref8 ref7 ref9 ref6 ref5 ref40 khalil (ref2) 1996 ref35 ref34 ref37 ref31 fossen (ref1) 2002 ref30 ref33 ref32 ref39 ref38 fei (ref36) 2012; 42 ref24 ref23 ref26 ref22 ref21 ref28 ref29 do (ref18) 2009 kristic (ref3) 1995
References_xml	– ident: ref28 doi: 10.1109/TNN.2008.2003290 – volume: 25 start-page: 635 year: 2014 ident: ref27 article-title: Reinforcement learning output feedback NN control using deterministic learning technique publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2013.2292704 – ident: ref17 doi: 10.1049/iet-cta.2011.0176 – volume: 42 start-page: 1599 year: 2012 ident: ref36 article-title: Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator publication-title: IEEE Trans Syst Man Cybern B Cybern doi: 10.1109/TSMCB.2012.2196039 – ident: ref24 doi: 10.1109/TNN.2010.2042611 – year: 1991 ident: ref4 publication-title: Applied nonlinear control – ident: ref35 doi: 10.1109/3477.537311 – ident: ref50 doi: 10.1142/S0129065710002486 – ident: ref38 doi: 10.1016/S0967-0661(02)00009-6 – ident: ref41 doi: 10.1109/TFUZZ.2003.819837 – ident: ref21 doi: 10.1109/TSMCA.2009.2030164 – ident: ref40 doi: 10.1007/0-8176-4470-9_23 – ident: ref13 doi: 10.1109/ACC.2009.5160064 – ident: ref39 doi: 10.1109/48.380245 – year: 1995 ident: ref3 publication-title: Nonlinear and Adaptive Control Design – ident: ref26 doi: 10.1109/TNNLS.2012.2225845 – volume: 22 start-page: 1328 year: 2011 ident: ref25 article-title: Adaptive neural output feedback controller design with reduced-order observer for a class of uncertain nonlinear SISO systems publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2011.2159865 – ident: ref54 doi: 10.1109/TIE.2008.926778 – ident: ref57 doi: 10.1109/TSMCB.2004.824525 – ident: ref8 doi: 10.1109/87.572132 – ident: ref12 doi: 10.1109/TIE.2008.2005933 – volume: 15 start-page: 182 year: 2013 ident: ref56 article-title: Intelligent adaptive law for missile guidance using fuzzy neural networks publication-title: Int J Fuzzy Syst – ident: ref53 doi: 10.1109/TNN.2007.899178 – ident: ref6 doi: 10.1016/j.neucom.2013.01.062 – ident: ref34 doi: 10.1109/TFUZZ.2004.825062 – ident: ref16 doi: 10.1049/ip-cta:20040902 – year: 1996 ident: ref2 publication-title: Nonlinear Systems – ident: ref22 doi: 10.1109/TSMCB.2011.2108283 – ident: ref49 doi: 10.1016/j.neucom.2009.05.006 – ident: ref14 doi: 10.1109/TCST.2002.806465 – ident: ref15 doi: 10.1016/j.automatica.2009.03.010 – ident: ref33 doi: 10.1109/TFUZZ.2003.814845 – ident: ref11 doi: 10.1016/S0005-1098(01)00199-6 – ident: ref10 doi: 10.1109/87.852916 – ident: ref37 doi: 10.1109/37.248001 – ident: ref51 doi: 10.1007/s11063-011-9181-1 – ident: ref7 doi: 10.1049/ip-cta:19971032 – ident: ref55 doi: 10.1109/TFUZZ.2011.2136349 – ident: ref30 doi: 10.1109/TIE.2013.2258299 – ident: ref23 doi: 10.1109/72.977306 – ident: ref52 doi: 10.1109/TFUZZ.2003.814833 – ident: ref32 doi: 10.1109/TIE.2007.893056 – ident: ref5 doi: 10.1016/j.automatica.2004.10.006 – ident: ref42 doi: 10.1109/TCST.2006.872507 – ident: ref43 doi: 10.1109/TII.2012.2205584 – ident: ref19 doi: 10.1109/TFUZZ.2003.812694 – ident: ref31 doi: 10.1109/TSMCB.2003.808177 – volume: 18 start-page: 150 year: 2010 ident: ref20 article-title: A novel robust adaptive-fuzzy-tracking control for a class of nonlinearmulti-input/multi-output systems publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2009.2038277 – ident: ref46 doi: 10.1002/0471781819 – year: 2009 ident: ref18 publication-title: Control of Ships and Underwater Vehicles Design for Underactuated and Nonlinear Marine Systems doi: 10.1007/978-1-84882-730-1 – year: 2002 ident: ref1 publication-title: Marine control systems Guidance navigation and control of ships rigs and underwater vehicles – ident: ref45 doi: 10.1109/TFUZZ.2011.2144600 – ident: ref29 doi: 10.1109/TSMCB.2002.1018767 – ident: ref47 doi: 10.1109/TNN.2004.841786 – ident: ref48 doi: 10.1016/j.fss.2004.07.007 – ident: ref9 doi: 10.1109/TCST.2010.2045654 – ident: ref44 doi: 10.1016/j.automatica.2010.02.024
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SubjectTerms	Adaptation models Adaptive robust tracking control Approximation methods Robustness Sea surface self-constructing fuzzy neural network (SCFNN) surface vehicle Uncertainty Vehicle dynamics Vehicles
Title	Self-Constructing Adaptive Robust Fuzzy Neural Tracking Control of Surface Vehicles With Uncertainties and Unknown Disturbances
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