Adaptive Control of Output Constrained Nonlinear Systems Based on Nonlinear Mapping
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- 英文论文题名：Adaptive Control of Output Constrained Nonlinear Systems Based on Nonlinear Mapping
- 论文作者：Ningning Wang ; Tianping Zhang ; Heqing Liu
- 英文论文作者：Ningning Wang ; Tianping Zhang ; Heqing Liu ; Department of Automation ; College of Information Engineering ; Yangzhou University
- 年：2017
- 作者机构：Department of Automation,College of Information Engineering,Yangzhou University;
- 英文论文关键词：Dynamic Surface Control ; Unmodeled Dynamics ; Output Constraint ; Nonlinear Mapping
- 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：TP273
- 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：2361k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, an adaptive dynamic surface output feedback control scheme is proposed for a class of nonlinear systems with unmodeled dynamics and output constraint. A description based on Lyapunov function for unmodeled dynamics is proposed to handle dynamic uncertainties. An one to one nonlinear mapping is introduced to solve output constrained problem. K-filters is introduced to estimate the unmeasured states. The radial basis function neural networks are utilized to approximate unknown nonlinear continuous function. It is shown that all the signals in the closed-loop system are bounded, and the output constraint is not violated. Simulation results demonstrate the effectiveness of the proposed approach.
In this paper, an adaptive dynamic surface output feedback control scheme is proposed for a class of nonlinear systems with unmodeled dynamics and output constraint. A description based on Lyapunov function for unmodeled dynamics is proposed to handle dynamic uncertainties. An one to one nonlinear mapping is introduced to solve output constrained problem. K-filters is introduced to estimate the unmeasured states. The radial basis function neural networks are utilized to approximate unknown nonlinear continuous function. It is shown that all the signals in the closed-loop system are bounded, and the output constraint is not violated. Simulation results demonstrate the effectiveness of the proposed approach.
In this paper, an adaptive dynamic surface output feedback control scheme is proposed for a class of nonlinear systems with unmodeled dynamics and output constraint. A description based on Lyapunov function for unmodeled dynamics is proposed to handle dynamic uncertainties. An one to one nonlinear mapping is introduced to solve output constrained problem. K-filters is introduced to estimate the unmeasured states. The radial basis function neural networks are utilized to approximate unknown nonlinear continuous function. It is shown that all the signals in the closed-loop system are bounded, and the output constraint is not violated. Simulation results demonstrate the effectiveness of the proposed approach.
引文
[1]B.S.Kim,S.G.Yoo,Approximation-based adaptive tracking control of nonlinear pure feedback systems with time-varying output constraints,Int.J.Control,Auto.and Syst.,13(2):257–265,2016.
[2]Y.J.Liu,S.C.Tong,Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints,Automatica,64:70–75,2016.
[3]K.P.Tee,B.B.Ren,S.S.Ge,Control of nonlinear systems with time-varying output constraints,Automatica,47(11):2511–2516,2011.
[4]S.I.Han,J.M.Lee,Adaptive fuzzy backstepping dynamic surface control for output constrained non-smooth nonlinear dynamic system,Int.J.Contr.,Auto.and Syst.,10(4):684–696,2012.
[5]Y.J.Liu,S.C.Tong,C.L.Philip Chen,Neural network control-based adaptive learning design for nonlinear systems with full-state constraints,IEEE Trans.Neural Netw.Learn.Syst.,27(7):1562–1571,2016.
[6]Y.Gao,S.C.Tong,Y.M.Li,Observer-based adaptive fuzzy output constrained control for mimo nonlinear systems with unknown control directions,Fuzzy Sets Syst.,290:79–99,2016.
[7]B.B.Ren,K.P.Tee,T.H.Lee,Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function,IEEE Trans.Neural Netw.,21(8):1339–1345,2010.
[8]Y.M.Li,S.C.Tong,T.S.Li,Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation,Fuzzy Sets Syst.,248:138–155,2014.
[9]W.He,Y.T.Dong,C.Y.Sun,Adaptive neural network control of unknown affine systems with input dead-zone and output constraint,ISA Trans.,58:96–104,2015.
[10]B.Niu B,L.N.Liu,Y.Y.Liu,Adaptive backstepping based fuzzy tracking control scheme for output-constrained nonlinear switched lower triangular systems with time delays,Neurocomputing,175:759–767,2016.
[11]W.C.Meng,Q.M.Yang,S.N.Si,and Y.X.Sun,Adaptive neural control of a class of output-constrained non-affine systems,IEEE Trans.on Cybern.,46(1):85–95,2016.
[12]T.Guo,and X.W.Wu,Backstepping control for outputconstrained nonlinear systems based on nonlinear mapping,Neural Computing and Applications,25:1665–1674,2014.
[13]S.Yin,H.Yu,R.Shahnazi,and A.Haghani,Fuzzy adaptive tracking control of constrained nonlinear switched stochastic pure feedback systems,IEEE Trans.on Cybern.,10.1109/TCYB.2016.2521179,2016.
[14]Z.P.Jiang,L.Praly,Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties,Automatica,34(7):825–840,1998.
[15]T.S.Chen,J.Hung,A small gain approach to global stabilization of nonlinear feedforward systems with input unmodeled dynamics,Automatica,46(6):1028–1034,2010.
[16]X.N.Xia,T.P.Zhang,Decentralized adaptive control for large-scale interconnected systems with dynamic uncertainties,Control Theory Appl.,32(3):347–356,2015.
[17]X.N.Xia,T.P.Zhang,Adaptive output feedback dynamic surface control of nonlinear systems with unmodeled dynamics and unknown high-frequency gain sign,Neurocomputing,143:312–321,2014.
[18]Z.P.Jiang,D.J.HILL,A robust adaptive backstepping scheme for nonlinear systems with unmodeled dynamics,IEEE Trans.Autom.Control,44(9):1705–1711,1999.
[19]T.P.Zhang,X.C.Shi,Q.Zhu,Y.Q.Yang,Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics,Neurocomputing,121:290–297,2013.
[20]X.N.Xia,T.P.Zhang,Y.Yi,Q.Q.Shen,Adaptive prescribed performance control of output feedback systems including input unmodeled dynamics,Neurocomputing,190:226–236,2016.
[21]M.Krstic,I.Kanellakopoulos,P.V.Kokotovic,Nonlinear and adaptive control design,Wiley:New York,1995:327–369.
[2]Y.J.Liu,S.C.Tong,Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints,Automatica,64:70–75,2016.
[3]K.P.Tee,B.B.Ren,S.S.Ge,Control of nonlinear systems with time-varying output constraints,Automatica,47(11):2511–2516,2011.
[4]S.I.Han,J.M.Lee,Adaptive fuzzy backstepping dynamic surface control for output constrained non-smooth nonlinear dynamic system,Int.J.Contr.,Auto.and Syst.,10(4):684–696,2012.
[5]Y.J.Liu,S.C.Tong,C.L.Philip Chen,Neural network control-based adaptive learning design for nonlinear systems with full-state constraints,IEEE Trans.Neural Netw.Learn.Syst.,27(7):1562–1571,2016.
[6]Y.Gao,S.C.Tong,Y.M.Li,Observer-based adaptive fuzzy output constrained control for mimo nonlinear systems with unknown control directions,Fuzzy Sets Syst.,290:79–99,2016.
[7]B.B.Ren,K.P.Tee,T.H.Lee,Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function,IEEE Trans.Neural Netw.,21(8):1339–1345,2010.
[8]Y.M.Li,S.C.Tong,T.S.Li,Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation,Fuzzy Sets Syst.,248:138–155,2014.
[9]W.He,Y.T.Dong,C.Y.Sun,Adaptive neural network control of unknown affine systems with input dead-zone and output constraint,ISA Trans.,58:96–104,2015.
[10]B.Niu B,L.N.Liu,Y.Y.Liu,Adaptive backstepping based fuzzy tracking control scheme for output-constrained nonlinear switched lower triangular systems with time delays,Neurocomputing,175:759–767,2016.
[11]W.C.Meng,Q.M.Yang,S.N.Si,and Y.X.Sun,Adaptive neural control of a class of output-constrained non-affine systems,IEEE Trans.on Cybern.,46(1):85–95,2016.
[12]T.Guo,and X.W.Wu,Backstepping control for outputconstrained nonlinear systems based on nonlinear mapping,Neural Computing and Applications,25:1665–1674,2014.
[13]S.Yin,H.Yu,R.Shahnazi,and A.Haghani,Fuzzy adaptive tracking control of constrained nonlinear switched stochastic pure feedback systems,IEEE Trans.on Cybern.,10.1109/TCYB.2016.2521179,2016.
[14]Z.P.Jiang,L.Praly,Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties,Automatica,34(7):825–840,1998.
[15]T.S.Chen,J.Hung,A small gain approach to global stabilization of nonlinear feedforward systems with input unmodeled dynamics,Automatica,46(6):1028–1034,2010.
[16]X.N.Xia,T.P.Zhang,Decentralized adaptive control for large-scale interconnected systems with dynamic uncertainties,Control Theory Appl.,32(3):347–356,2015.
[17]X.N.Xia,T.P.Zhang,Adaptive output feedback dynamic surface control of nonlinear systems with unmodeled dynamics and unknown high-frequency gain sign,Neurocomputing,143:312–321,2014.
[18]Z.P.Jiang,D.J.HILL,A robust adaptive backstepping scheme for nonlinear systems with unmodeled dynamics,IEEE Trans.Autom.Control,44(9):1705–1711,1999.
[19]T.P.Zhang,X.C.Shi,Q.Zhu,Y.Q.Yang,Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics,Neurocomputing,121:290–297,2013.
[20]X.N.Xia,T.P.Zhang,Y.Yi,Q.Q.Shen,Adaptive prescribed performance control of output feedback systems including input unmodeled dynamics,Neurocomputing,190:226–236,2016.
[21]M.Krstic,I.Kanellakopoulos,P.V.Kokotovic,Nonlinear and adaptive control design,Wiley:New York,1995:327–369.