Non-fragile state estimation for discrete-time neural network system with randomly occurring sensor saturations
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- 英文论文题名：Non-fragile state estimation for discrete-time neural network system with randomly occurring sensor saturations
- 论文作者：Bao Wendong ; Li Jianning ; Lv Meilei ; Cai Jianping ; Wen Chenglin
- 英文论文作者：Bao Wendong ; Li Jianning ; Lv Meilei ; Cai Jianping ; Wen Chenglin ; School of Automation ; Hangzhou Dianzi University ; Institute of Electrical and Information Engineering ; Quzhou University
- 年：2017
- 作者机构：School of Automation,Hangzhou Dianzi University;Institute of Electrical and Information Engineering,Quzhou University;
- 英文论文关键词：Non-fragile state estimation ; Time delays ; Nonlinearity ; Sensor saturations
- 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（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TP183;TP212
- 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：179k
- 原文格式：D
- 会议级别：全国
摘要
This paper investigates the problem for the non-fragile state estimation of discrete-time neural network system with randomly occurring sensor saturations and time delays. In order to show the possible gain variations occurring in complex environments, a non-fragile state estimator is designed to ensure the estimation error converges to zero exponentially. And,by using a sensor saturation function to deals with the sensor saturation phenomenon.Then, Lyapunnov-Krasovskii functional approach is proposed, sufficient conditions are established to guarantee the existence of the desired state estimator. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
This paper investigates the problem for the non-fragile state estimation of discrete-time neural network system with randomly occurring sensor saturations and time delays. In order to show the possible gain variations occurring in complex environments, a non-fragile state estimator is designed to ensure the estimation error converges to zero exponentially. And,by using a sensor saturation function to deals with the sensor saturation phenomenon.Then, Lyapunnov-Krasovskii functional approach is proposed, sufficient conditions are established to guarantee the existence of the desired state estimator. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
This paper investigates the problem for the non-fragile state estimation of discrete-time neural network system with randomly occurring sensor saturations and time delays. In order to show the possible gain variations occurring in complex environments, a non-fragile state estimator is designed to ensure the estimation error converges to zero exponentially. And,by using a sensor saturation function to deals with the sensor saturation phenomenon.Then, Lyapunnov-Krasovskii functional approach is proposed, sufficient conditions are established to guarantee the existence of the desired state estimator. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
引文
[1]F.M.Salam,J.Zhang,Adaptive neural observer with forward co-state propagation,in:Proceedings of the International Joint Conference on Neural Net-works(IJCNN’01),Washington,USA,vol.1,2001,pp.675C680.
[2]Farahani M,Zare Bidaki A R,Enshaeieh M.Intelligent control of a DC motor using a self-constructing wavelet neural network.Systems Science&Control Engineering:An Open Access Journal,2014,2(1):261-267.
[3]Wang Z,Liu Y,Liu X.On global asymptotic stability of neural networks with discrete and distributed delays.Physics Letters A,2005,345(4):299-308.
[4]Habtom R,Litz L.Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter//Neural Networks,1997.International Conference on.IEEE,1997,4:2067-2071.
[5]Shin Y C.Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems.IEEE Transactions on Neural Networks,1994,5(4):594-603.
[6]Huang H,Feng G,Cao J.Robust state estimation for uncertain neural networks with time-varying delay.IEEE Transactions on Neural Networks,2008,19(8):1329-1339.
[7]Wang T,Gao H,Qiu J.A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control.IEEE Transactions on Neural Networks and Learning Systems,2016,27(2):416-425.
[8]Li H,Chen B,Zhou Q,et al.Robust exponential stability for uncertain stochastic neural networks with discrete and distributed time-varying delays.Physics Letters A,2008,372(19):3385-3394.
[9]Li X,Wang X,Chen G.Pinning a complex dynamical network to its equilibrium.IEEE Transactions on Circuits and Systems I:Regular Papers,2004,51(10):2074-2087.
[10]Porfiri M,Di Bernardo M.Criteria for global pinningcontrollability of complex networks.Automatica,2008,44(12):3100-3106.
[11]Song Q,Cao J.On pinning synchronization of directed and undirected complex dynamical networks.IEEE Transactions on Circuits and Systems I:Regular Papers,2010,57(3):672-680.
[12]Wang X,Li X,Lu J.Control and flocking of networked systems via pinning.IEEE Circuits and Systems Magazine,2010,10(3):83-91.
[13]Hou N,Dong H,Wang Z,et al.Non-fragile state estimation for discrete Markovian jumping neural networks.Neurocomputing,2016,179:238-245.
[14]Shao H.Improved delay-dependent globally asymptotic stability criteria for neural networks with a constant delay.IEEETransactions on Circuits and Systems II:Express Briefs,2008,55(10):1071-1075.
[15]Arik S.Global robust stability analysis of neural networks with discrete time delays.Chaos,Solitons&Fractals,2005,26(5):1407-1414.
[16]Song Q,Cao J.Dynamics of bidirectional associative memory networks with distributed delays and reaction Cdiffusion terms.Nonlinear Analysis:Real World Applications,2007,8(1):345-361.
[17]Song Q.Synchronization analysis in an array of asymmetric neural networks with time-varying delays and nonlinear coupling.Applied Mathematics and Computation,2010,216(5):1605-1613.
[18]Balasubramaniam P,Nagamani G.A delay decomposition approach to delay-dependent passivity analysis for interval neural networks with time-varying delay.Neurocomputing,2011,74(10):1646-1653.
[19]Kwon O M,Park M J,Lee S M,et al.Stability for neural networks with time-varying delays via some new approaches.IEEE transactions on neural networks and learning systems,2013,24(2):181-193.
[20]Chen Z,Yang M.Exponential convergence for HRNNs with continuously distributed delays in the leakage terms.Neural Computing and Applications,2013,23(7-8):2221-2229.
[21]Garcia G,Tarbouriech S,Eckhard D.Finite L2 gain and internal stabilisation of linear systems subject to actuator and sensor saturations.IET control theory&applications,2009,3(7):799-812.
[22]Yang F,Li Y.Set-membership filtering for systems with sensor saturation.Automatica,2009,45(8):1896-1902.
[23]Cao Y Y,Lin Z,Chen B M.An output feedback H controller design for linear systems subject to sensor nonlinearities.IEEETransactions on Circuits and Systems I:Fundamental Theory and Applications,2003,50(7):914-921.
[24]Khalil H K,Grizzle J W.Nonlinear systems.New Jersey:Prentice hall,1996.
[25]Hu T,Lin Z.Control systems with actuator saturation:analysis and design.Springer Science&Business Media,2001.
[26]Koplon R,Sontag E D,Hautus M L J.Observability of linear systems with saturated outputs.Linear algebra and its applications,1994,205:909-936.
[27]Kreisselmeier G.Stabilization of linear systems in the presence of output measurement saturation.Systems&Control Letters,1996,29(1):27-30.
[28]Li J N,Pan Y J,Su H Y,et al.Stochastic reliable control of a class of networked control systems with actuator faults and input saturation.International Journal of Control,Automation and Systems,2014,12(3):564-571.
[29]Boyd S P,El Ghaoui L,Feron E,et al.Linear matrix inequalities in system and control theory.Philadelphia:Society for industrial and applied mathematics,1994.
[2]Farahani M,Zare Bidaki A R,Enshaeieh M.Intelligent control of a DC motor using a self-constructing wavelet neural network.Systems Science&Control Engineering:An Open Access Journal,2014,2(1):261-267.
[3]Wang Z,Liu Y,Liu X.On global asymptotic stability of neural networks with discrete and distributed delays.Physics Letters A,2005,345(4):299-308.
[4]Habtom R,Litz L.Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter//Neural Networks,1997.International Conference on.IEEE,1997,4:2067-2071.
[5]Shin Y C.Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems.IEEE Transactions on Neural Networks,1994,5(4):594-603.
[6]Huang H,Feng G,Cao J.Robust state estimation for uncertain neural networks with time-varying delay.IEEE Transactions on Neural Networks,2008,19(8):1329-1339.
[7]Wang T,Gao H,Qiu J.A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control.IEEE Transactions on Neural Networks and Learning Systems,2016,27(2):416-425.
[8]Li H,Chen B,Zhou Q,et al.Robust exponential stability for uncertain stochastic neural networks with discrete and distributed time-varying delays.Physics Letters A,2008,372(19):3385-3394.
[9]Li X,Wang X,Chen G.Pinning a complex dynamical network to its equilibrium.IEEE Transactions on Circuits and Systems I:Regular Papers,2004,51(10):2074-2087.
[10]Porfiri M,Di Bernardo M.Criteria for global pinningcontrollability of complex networks.Automatica,2008,44(12):3100-3106.
[11]Song Q,Cao J.On pinning synchronization of directed and undirected complex dynamical networks.IEEE Transactions on Circuits and Systems I:Regular Papers,2010,57(3):672-680.
[12]Wang X,Li X,Lu J.Control and flocking of networked systems via pinning.IEEE Circuits and Systems Magazine,2010,10(3):83-91.
[13]Hou N,Dong H,Wang Z,et al.Non-fragile state estimation for discrete Markovian jumping neural networks.Neurocomputing,2016,179:238-245.
[14]Shao H.Improved delay-dependent globally asymptotic stability criteria for neural networks with a constant delay.IEEETransactions on Circuits and Systems II:Express Briefs,2008,55(10):1071-1075.
[15]Arik S.Global robust stability analysis of neural networks with discrete time delays.Chaos,Solitons&Fractals,2005,26(5):1407-1414.
[16]Song Q,Cao J.Dynamics of bidirectional associative memory networks with distributed delays and reaction Cdiffusion terms.Nonlinear Analysis:Real World Applications,2007,8(1):345-361.
[17]Song Q.Synchronization analysis in an array of asymmetric neural networks with time-varying delays and nonlinear coupling.Applied Mathematics and Computation,2010,216(5):1605-1613.
[18]Balasubramaniam P,Nagamani G.A delay decomposition approach to delay-dependent passivity analysis for interval neural networks with time-varying delay.Neurocomputing,2011,74(10):1646-1653.
[19]Kwon O M,Park M J,Lee S M,et al.Stability for neural networks with time-varying delays via some new approaches.IEEE transactions on neural networks and learning systems,2013,24(2):181-193.
[20]Chen Z,Yang M.Exponential convergence for HRNNs with continuously distributed delays in the leakage terms.Neural Computing and Applications,2013,23(7-8):2221-2229.
[21]Garcia G,Tarbouriech S,Eckhard D.Finite L2 gain and internal stabilisation of linear systems subject to actuator and sensor saturations.IET control theory&applications,2009,3(7):799-812.
[22]Yang F,Li Y.Set-membership filtering for systems with sensor saturation.Automatica,2009,45(8):1896-1902.
[23]Cao Y Y,Lin Z,Chen B M.An output feedback H controller design for linear systems subject to sensor nonlinearities.IEEETransactions on Circuits and Systems I:Fundamental Theory and Applications,2003,50(7):914-921.
[24]Khalil H K,Grizzle J W.Nonlinear systems.New Jersey:Prentice hall,1996.
[25]Hu T,Lin Z.Control systems with actuator saturation:analysis and design.Springer Science&Business Media,2001.
[26]Koplon R,Sontag E D,Hautus M L J.Observability of linear systems with saturated outputs.Linear algebra and its applications,1994,205:909-936.
[27]Kreisselmeier G.Stabilization of linear systems in the presence of output measurement saturation.Systems&Control Letters,1996,29(1):27-30.
[28]Li J N,Pan Y J,Su H Y,et al.Stochastic reliable control of a class of networked control systems with actuator faults and input saturation.International Journal of Control,Automation and Systems,2014,12(3):564-571.
[29]Boyd S P,El Ghaoui L,Feron E,et al.Linear matrix inequalities in system and control theory.Philadelphia:Society for industrial and applied mathematics,1994.
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