Decentralized Adaptive Dynamic Surface Control of Nonlinear Systems with Output Constraints
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- 英文论文题名：Decentralized Adaptive Dynamic Surface Control of Nonlinear Systems with Output Constraints
- 论文作者：GE Jiwei ; ZHANG Tianping ; XIA Xiaonan ; XIA Meizhen
- 英文论文作者：GE Jiwei ; ZHANG Tianping ; XIA Xiaonan ; XIA Meizhen ; Department of Automation ; College of Information Engineering
- 年：2017
- 作者机构：Department of Automation,College of Information Engineering;
- 英文论文关键词：Unmodeled Dynamics ; Output Constraints ; Dynamic Surface Control(DSC) ; Integral Barrier Lyapunov Function(iBLF)
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：O231
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：15998k
- 原文格式：D
- 会议级别：全国
摘要
Using modified dynamic surface control(DSC) method and the approximation capability of neural networks, decentralized adaptive DSC is developed for a class of pure-feedback nonlinear interconnected systems with state unmodeled dynamics and output constraints. Dynamic signal is introduced to deal with the dynamic uncertainty produced by unmodeled dynamics. The integral barrier Lyapunov function(i BLF) is used to handle the output constraints. By theoretical analysis, it is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the output constraints are not violated.
Using modified dynamic surface control(DSC) method and the approximation capability of neural networks, decentralized adaptive DSC is developed for a class of pure-feedback nonlinear interconnected systems with state unmodeled dynamics and output constraints. Dynamic signal is introduced to deal with the dynamic uncertainty produced by unmodeled dynamics. The integral barrier Lyapunov function(i BLF) is used to handle the output constraints. By theoretical analysis, it is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the output constraints are not violated.
Using modified dynamic surface control(DSC) method and the approximation capability of neural networks, decentralized adaptive DSC is developed for a class of pure-feedback nonlinear interconnected systems with state unmodeled dynamics and output constraints. Dynamic signal is introduced to deal with the dynamic uncertainty produced by unmodeled dynamics. The integral barrier Lyapunov function(i BLF) is used to handle the output constraints. By theoretical analysis, it is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the output constraints are not violated.
引文
[1]Y.J.Liu,S.C.Tong,T.S.LI,Observer-based adaptive fuzzy tracking control for a class of uncertain nonlinear MIMO systems,Fuzzy Sets and System,164(1):25–44,2011.
[2]T.P.Zhang,S.S.Ge,Adaptive neural network tracking control of MIMO nonlinear systems with unknown dead zones and control directions,IEEE Transactions on Neural Networks,20(3):483-497,2009.
[3]Y.S.Huang,M.Wu,Robust decentralized direct adaptive output feedback fuzzy control for a class of large-sale non-affine nonlinear systems,Information Sciences,181(11):2392-2404,2014.
[4]S.J.Yoo,N.Hovakimyan,C.Cao,Decentralized 1L adaptive control for large-scale nonlinear systems with interconnected unmodeled dynamics,IET Control Theory and Applications,4(10):1972-1988,2010.
[5]Y.S.Liu,X.Y.LI,Decentralized robust adaptive control of nonlinear systems with unmodeled dynamics,IEEE Transactions on Automatic Control,47(5):848-856,2002.
[6]S.C.Tong,Y.M.LI,Adaptive fuzzy output feedback control of MIMO nonlinear systems with unknown dead-zone inputs,IEEE Transactions on Fuzzy Systems,21(1):134-146,2013.
[7]H.B.Zhu,T.P.Zhang,X.N.Xia,Decentralized adaptive dynamic surface control of large-scale interconnected systems with unmodeled dynamics,in:Proceedings of the33rd Chinese Control Conference.New York:IEEE,2014:2080–2085.
[8]X.N.Xia,T.P.Zhang,Decentralized adaptive control for large-scale interconnected systems with dynamic uncertainties,Control Theory and Applications,32(3):341-356,2015.
[9]S.Mehraeen,S.Jagannathan,L.C.Mariesa,Decentralized dynamic surface control of large-scale interconnected systems in strict-feedback form using neural networks with asymptotic stabilization,IEEE Transactions on Neural Networks,22(11):1709-1722,2011.
[10]D.Wang,Z.H.Peng;T.S.Li;X.Q.Li;G.Sun,Adaptive dynamic surface control for a class of uncertain nonlinear systems in pure-feedback form,in Proceedings of the IEEE Conference on Decision and Control,Shanghai,2009:1956-1961.
[11]K.P.Tee,S.S.Ge,E.H.Tay,Barrier Lyapunov functions for the control of output-constrained nonlinear systems,Automatica,45(4):918-927,2009.
[12]K.P.Tee,S.S.Ge,Control of nonlinear systems with full state constraint using a barrier Lyapunov function,IEEE Conference on Decision&Control,2009:8618-8623.
[13]K.P.Tee,S.S.Ge,Control of nonlinear systems with partial state constraints using a barrier Lyapunov function,International Journal of Control,84(12):2008-2023,2011.
[14]Y.J.Liu,S.Tong,Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints,Automatica,64:70-75,2016.
[15]K.P.Tee,S.S.Ge,Control of state-constrained nonlinear systems using integral barrier Lyapunov functionals,IEEE Conference on Decision and Control,2012:3239-3244.
[16]B.Ren,S.S.Ge,K.P.Tee,T.H.Lee,Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function,IEEE Transactions on Neural Networks,21(8):1339-1345,2010.
[17]W.He,Y.Chen,Z.Yin,Adaptive neural network control of an uncertain robot with full-state constraints,IEEE Transactions on Cybernetics,46(3):620-629,2016.
[18]W.He,Y.Dong,C.Sun,Adaptive neural network control of unknown nonlinear affine systems with input dead-zone and output constraint,ISA Transactions,58:96-104,2015.
[19]Y.Li,T.Li,X.Jing.Indirect adaptive fuzzy control for input and output constrained nonlinear systems using a barrier Lyapunov function.International Journal of Adaptive Control and Signal Processing,28(2):184–199,2014.
[20]Y.Li,S.Tong,T.Li.Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation.Fuzzy Sets&Systems,248:138-155,2014.
[2]T.P.Zhang,S.S.Ge,Adaptive neural network tracking control of MIMO nonlinear systems with unknown dead zones and control directions,IEEE Transactions on Neural Networks,20(3):483-497,2009.
[3]Y.S.Huang,M.Wu,Robust decentralized direct adaptive output feedback fuzzy control for a class of large-sale non-affine nonlinear systems,Information Sciences,181(11):2392-2404,2014.
[4]S.J.Yoo,N.Hovakimyan,C.Cao,Decentralized 1L adaptive control for large-scale nonlinear systems with interconnected unmodeled dynamics,IET Control Theory and Applications,4(10):1972-1988,2010.
[5]Y.S.Liu,X.Y.LI,Decentralized robust adaptive control of nonlinear systems with unmodeled dynamics,IEEE Transactions on Automatic Control,47(5):848-856,2002.
[6]S.C.Tong,Y.M.LI,Adaptive fuzzy output feedback control of MIMO nonlinear systems with unknown dead-zone inputs,IEEE Transactions on Fuzzy Systems,21(1):134-146,2013.
[7]H.B.Zhu,T.P.Zhang,X.N.Xia,Decentralized adaptive dynamic surface control of large-scale interconnected systems with unmodeled dynamics,in:Proceedings of the33rd Chinese Control Conference.New York:IEEE,2014:2080–2085.
[8]X.N.Xia,T.P.Zhang,Decentralized adaptive control for large-scale interconnected systems with dynamic uncertainties,Control Theory and Applications,32(3):341-356,2015.
[9]S.Mehraeen,S.Jagannathan,L.C.Mariesa,Decentralized dynamic surface control of large-scale interconnected systems in strict-feedback form using neural networks with asymptotic stabilization,IEEE Transactions on Neural Networks,22(11):1709-1722,2011.
[10]D.Wang,Z.H.Peng;T.S.Li;X.Q.Li;G.Sun,Adaptive dynamic surface control for a class of uncertain nonlinear systems in pure-feedback form,in Proceedings of the IEEE Conference on Decision and Control,Shanghai,2009:1956-1961.
[11]K.P.Tee,S.S.Ge,E.H.Tay,Barrier Lyapunov functions for the control of output-constrained nonlinear systems,Automatica,45(4):918-927,2009.
[12]K.P.Tee,S.S.Ge,Control of nonlinear systems with full state constraint using a barrier Lyapunov function,IEEE Conference on Decision&Control,2009:8618-8623.
[13]K.P.Tee,S.S.Ge,Control of nonlinear systems with partial state constraints using a barrier Lyapunov function,International Journal of Control,84(12):2008-2023,2011.
[14]Y.J.Liu,S.Tong,Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints,Automatica,64:70-75,2016.
[15]K.P.Tee,S.S.Ge,Control of state-constrained nonlinear systems using integral barrier Lyapunov functionals,IEEE Conference on Decision and Control,2012:3239-3244.
[16]B.Ren,S.S.Ge,K.P.Tee,T.H.Lee,Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function,IEEE Transactions on Neural Networks,21(8):1339-1345,2010.
[17]W.He,Y.Chen,Z.Yin,Adaptive neural network control of an uncertain robot with full-state constraints,IEEE Transactions on Cybernetics,46(3):620-629,2016.
[18]W.He,Y.Dong,C.Sun,Adaptive neural network control of unknown nonlinear affine systems with input dead-zone and output constraint,ISA Transactions,58:96-104,2015.
[19]Y.Li,T.Li,X.Jing.Indirect adaptive fuzzy control for input and output constrained nonlinear systems using a barrier Lyapunov function.International Journal of Adaptive Control and Signal Processing,28(2):184–199,2014.
[20]Y.Li,S.Tong,T.Li.Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation.Fuzzy Sets&Systems,248:138-155,2014.