Adaptive neural control of nonlinear time-delay systems with full state constraints and unmodeled dynamics
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- 英文论文题名：Adaptive neural control of nonlinear time-delay systems with full state constraints and unmodeled dynamics
- 论文作者：Xia Mei-zhen ; Zhang Tian-ping
- 英文论文作者：Xia Mei-zhen ; Zhang Tian-ping ; 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：full state constraints ; unmodeled dynamics ; integral barrier Lyapunov function ; backstepping
- 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：TP13
- 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：7
- 文件大小：435k
- 原文格式：D
- 会议级别：全国
摘要
An adaptive neural control method is developed for a class of full state constrained nonlinear systems with unmodeled dynamics and time-varying delays in this paper. An integral barrier Lyapunov function(i BLF) is used to every step in the backstepping procedure to ensure that the full state constraints are not violated. The unmodeled dynamics is dealt with by introducing a dynamic signal and the time-varying delays is disposed through constructing appropriate Lyapunov-Krasovskii functions. Moreover, it is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded,and the full state constraints are not violated. A simulation example further demonstrates the effectiveness of the proposed control scheme.
An adaptive neural control method is developed for a class of full state constrained nonlinear systems with unmodeled dynamics and time-varying delays in this paper. An integral barrier Lyapunov function(i BLF) is used to every step in the backstepping procedure to ensure that the full state constraints are not violated. The unmodeled dynamics is dealt with by introducing a dynamic signal and the time-varying delays is disposed through constructing appropriate Lyapunov-Krasovskii functions. Moreover, it is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded,and the full state constraints are not violated. A simulation example further demonstrates the effectiveness of the proposed control scheme.
An adaptive neural control method is developed for a class of full state constrained nonlinear systems with unmodeled dynamics and time-varying delays in this paper. An integral barrier Lyapunov function(i BLF) is used to every step in the backstepping procedure to ensure that the full state constraints are not violated. The unmodeled dynamics is dealt with by introducing a dynamic signal and the time-varying delays is disposed through constructing appropriate Lyapunov-Krasovskii functions. Moreover, it is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded,and the full state constraints are not violated. A simulation example further demonstrates the effectiveness of the proposed control scheme.
引文
[1]K.Tee,S.Ge,E.Tay,Barrier Lyapunov functions for the control of output-constrained nonlinear systems,Automatica,45(4):918–927,2009.
[2]K.Tee,B.Ren and S.S.Ge,Control of nonlinear systems with time-varying output constraints,Automatica,47(11):2511–2516,2011.
[3]K.Tee,S.Ge,Control of nonlinear systems with partial state constraints using a barrier Lyapunov function,International Journal of Control,84(12):2008–2023,2011.
[4]Y.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.
[5]B.Kim,S.Yoo,Approximation-based adaptive control of uncertain non-linear pure-feedback systems with full state constraints,IET Control Theory Application,8(17):2070–2081,2014.
[6]D.Li,J.Li and S.Li,Adaptive control of nonlinear systems with full state constraints using Integral Barrier Lyapunov Functionals,Neurocomputing,186:90–96,2016.
[7]Z.Tang,S.S.Ge,K.Tee,and W.He,Robust Adaptive Neural Tracking Control for a Class of Perturbed Uncertain Nonlinear Systems With State Constraints,IEEE Trans.on Systems,Man and Cybernetics Systems:Systems,46(12):1618–1629,2016.
[8]M.Krstic,I.Kanellakopoulos,and P.V.Kokotovic,Nonlinear and Adaptive Control Design.Wiley,1995.
[9]Z.Jiang,L.Praly,Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties,Automatica,34(7):825–840,1998.
[10]Z.Jiang,D.Hill,A robust adaptive backstepping scheme for nonlinear systems with unmodeled dynamics,IEEE Trans.on Automatic Control,44(9):1705–1711,1999.
[11]S.Tong,X.He,Y.Li and H.Zhang,Adaptive fuzzy backstepping robust control for uncertain nonlinear systems based on small-gain approach,Fuzzy Sets Systems,161(3):771–796,2010.
[12]S.Tong,Y.Li,Fuzzy adaptive robust backstepping stabilization for SISO nonlinear systems with unknown virtual control direction,Information Sciences,180(23):4619–4640,2010.
[13]J.Hale,Theory of Functional Differential Equations,Springer,New York,1977.
[14]M.Jankovic,Control Lyapunov–Razumikhin functions and robust stabilization of time delay systems,IEEE Trans on Automatic Control,46(7):1048–1060,2001.
[15]M.Wang,B.Chen,X.Liu and P.Shi,Adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear time-delay systems,Fuzzy Sets and Systems,159(3):949–967,2008.
[16]M.Wang,B.Chen,K.Liu and S.Zhang,Adaptive fuzzy tracking control of nonlinear time-delay systems with unknown virtual control coefficients,Information Sciences,,178(22):4326–4340,2008.
[17]M.Wang,S.Ge and K.Hong,Approximation-based adaptive trackingcontrol of pure-feedback nonlinear systems with multiple unknown time-varying delays,IEEE Trans.on Neural Networks,21(11):1804–1816,2010.
[18]T.Zhang,X.Xia and J.Zhu,Adaptive neural control of state delayed non-linear systems with unmodelled dynamics and distributed time-varying delays,IET Control Theory&Applications,8(12):1071–1082,2014.
[19]C.Hua,X.Guan,Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks,IEEE Trans.on Neural Networks,19(4):673–688,2008.
[20]Y.Liu,J.Li,S.Tong and C.Chen Neural Network ControlBased Adaptive Learning Design for Nonlinear Systems With Full-State Constraints,IEEE Trans.on Neural Networks&Learning Systems,27(7):1-10,2016.
[21]S.S.Ge,C.Hang and T.Zhang,Stable adaptive control of nonlinear multivariable systems with triangular control structure,IEEE Trans.on Automatic Control,45(6):1221–1225,2000.
[2]K.Tee,B.Ren and S.S.Ge,Control of nonlinear systems with time-varying output constraints,Automatica,47(11):2511–2516,2011.
[3]K.Tee,S.Ge,Control of nonlinear systems with partial state constraints using a barrier Lyapunov function,International Journal of Control,84(12):2008–2023,2011.
[4]Y.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.
[5]B.Kim,S.Yoo,Approximation-based adaptive control of uncertain non-linear pure-feedback systems with full state constraints,IET Control Theory Application,8(17):2070–2081,2014.
[6]D.Li,J.Li and S.Li,Adaptive control of nonlinear systems with full state constraints using Integral Barrier Lyapunov Functionals,Neurocomputing,186:90–96,2016.
[7]Z.Tang,S.S.Ge,K.Tee,and W.He,Robust Adaptive Neural Tracking Control for a Class of Perturbed Uncertain Nonlinear Systems With State Constraints,IEEE Trans.on Systems,Man and Cybernetics Systems:Systems,46(12):1618–1629,2016.
[8]M.Krstic,I.Kanellakopoulos,and P.V.Kokotovic,Nonlinear and Adaptive Control Design.Wiley,1995.
[9]Z.Jiang,L.Praly,Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties,Automatica,34(7):825–840,1998.
[10]Z.Jiang,D.Hill,A robust adaptive backstepping scheme for nonlinear systems with unmodeled dynamics,IEEE Trans.on Automatic Control,44(9):1705–1711,1999.
[11]S.Tong,X.He,Y.Li and H.Zhang,Adaptive fuzzy backstepping robust control for uncertain nonlinear systems based on small-gain approach,Fuzzy Sets Systems,161(3):771–796,2010.
[12]S.Tong,Y.Li,Fuzzy adaptive robust backstepping stabilization for SISO nonlinear systems with unknown virtual control direction,Information Sciences,180(23):4619–4640,2010.
[13]J.Hale,Theory of Functional Differential Equations,Springer,New York,1977.
[14]M.Jankovic,Control Lyapunov–Razumikhin functions and robust stabilization of time delay systems,IEEE Trans on Automatic Control,46(7):1048–1060,2001.
[15]M.Wang,B.Chen,X.Liu and P.Shi,Adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear time-delay systems,Fuzzy Sets and Systems,159(3):949–967,2008.
[16]M.Wang,B.Chen,K.Liu and S.Zhang,Adaptive fuzzy tracking control of nonlinear time-delay systems with unknown virtual control coefficients,Information Sciences,,178(22):4326–4340,2008.
[17]M.Wang,S.Ge and K.Hong,Approximation-based adaptive trackingcontrol of pure-feedback nonlinear systems with multiple unknown time-varying delays,IEEE Trans.on Neural Networks,21(11):1804–1816,2010.
[18]T.Zhang,X.Xia and J.Zhu,Adaptive neural control of state delayed non-linear systems with unmodelled dynamics and distributed time-varying delays,IET Control Theory&Applications,8(12):1071–1082,2014.
[19]C.Hua,X.Guan,Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks,IEEE Trans.on Neural Networks,19(4):673–688,2008.
[20]Y.Liu,J.Li,S.Tong and C.Chen Neural Network ControlBased Adaptive Learning Design for Nonlinear Systems With Full-State Constraints,IEEE Trans.on Neural Networks&Learning Systems,27(7):1-10,2016.
[21]S.S.Ge,C.Hang and T.Zhang,Stable adaptive control of nonlinear multivariable systems with triangular control structure,IEEE Trans.on Automatic Control,45(6):1221–1225,2000.