Identification of Distributed Parameter System with Recurrent Trajectory via Deterministic Learning
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- 英文论文题名：Identification of Distributed Parameter System with Recurrent Trajectory via Deterministic Learning
- 论文作者：Xunde Dong ; Cong Wang ; Wenjie Si
- 英文论文作者：Xunde Dong ; Cong Wang ; Wenjie Si ; College of Automation Science and Engineering ; South China University of Technology
- 年：2017
- 作者机构：College of Automation Science and Engineering,South China University of Technology;
- 英文论文关键词：Distributed parameter system ; System identification ; Deterministic learning ; Recurrent trajectory
- 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：N945.14
- 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：1881k
- 原文格式：D
- 会议级别：全国
摘要
In the paper, a novel method for identification of distributed parameter system(DPS) with recurrent trajectory via deterministic learning is proposed. The system dynamics rather than the system parameters or system structure is identified,which is completely different from the existing literature. The DPS, which is often described by partial differential equation(PDE), is first transformed into a set of ordinary differential equations(ODEs) by using a special case of finite difference method,method of lines. Then, the system dynamics at each discrete node is identified by using deterministic learning. Without loss of generality, the DPS dynamics at any spatial point in the spatial domain Ω can also be accurately identified by using the proposed method. It will be more appropriate for DPS identification in many physical systems whose both structure and parameters are unknown. Simulation results have shown the feasibility and efficient of the proposed method.
In the paper, a novel method for identification of distributed parameter system(DPS) with recurrent trajectory via deterministic learning is proposed. The system dynamics rather than the system parameters or system structure is identified,which is completely different from the existing literature. The DPS, which is often described by partial differential equation(PDE), is first transformed into a set of ordinary differential equations(ODEs) by using a special case of finite difference method,method of lines. Then, the system dynamics at each discrete node is identified by using deterministic learning. Without loss of generality, the DPS dynamics at any spatial point in the spatial domain Ω can also be accurately identified by using the proposed method. It will be more appropriate for DPS identification in many physical systems whose both structure and parameters are unknown. Simulation results have shown the feasibility and efficient of the proposed method.
In the paper, a novel method for identification of distributed parameter system(DPS) with recurrent trajectory via deterministic learning is proposed. The system dynamics rather than the system parameters or system structure is identified,which is completely different from the existing literature. The DPS, which is often described by partial differential equation(PDE), is first transformed into a set of ordinary differential equations(ODEs) by using a special case of finite difference method,method of lines. Then, the system dynamics at each discrete node is identified by using deterministic learning. Without loss of generality, the DPS dynamics at any spatial point in the spatial domain Ω can also be accurately identified by using the proposed method. It will be more appropriate for DPS identification in many physical systems whose both structure and parameters are unknown. Simulation results have shown the feasibility and efficient of the proposed method.
引文
[1]D.Ucinski,Optimal measurement methods for distributed parameter system identification.Boca Raton:CRC Press,2004.
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[15]J.Chang,Identification of variable coefficients for vibrating systems by boundary control and observation,Journal of Control Theory and Applications,6(2):127–132,2008.
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[17]C.Wang and D.J.Hill,Deterministic learning and rapid dynamical pattern recognition,Neural Networks,IEEE Transactions on,18(3):617–630,2007.
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[2]D.Coca and S.A.Billings,Identification of finite dimensional models of infinite dimensional dynamical systems,Automatica,38(11):1851–1865,2002.
[3]D.Coca and S.A.Billings,Direct parameter identification of distributed parameter systems,International Journal of Systems Science,31(1):11–17,2000.
[4]H.T.Banks and K.Kunisch,Estimation techniques for distributed parameter systems.Berlin:Springer Science&Business Media,2012.
[5]M.Demetriou and I.Rosen,Adaptive identification of second-order distributed parameter systems,Inverse Problems,10(2):261,1994.
[6]M.Demetriou and I.Rosen,On-line robust parameter identification for parabolic systems,International Journal of Adaptive Control and Signal Processing,15(6):615–631,2001.
[7]K.H.Hoffmann and J.Sprekels,On the identification of parameters in general variational inequalities by asymptotic regularization,SIAM journal on mathematical analysis,17(5):1198–1217,1986.
[8]M.Grdiac and P.Paris,Direct identification of elastic constants of anisotropic plates by modal analysis:theoretical and numerical aspects,Journal of sound and vibration,195(3):401–415,1996.
[9]L.Guo,S.Billings,and H.Wei,Estimation of spatial derivatives and identification of continuous spatio-temporal dynamical systems,International Journal of Control,79:(9):111–1135,2006.
[10]R.Rannacher and B.Vexler,A priori error estimates for the finite element discretization of elliptic parameter identification problems with pointwise measurements,SIAM Journal on Control and Optimization,44(5):1844–1863,2005.
[11]H.Park,T.Kim,and D.Cho,Estimation of parameters in flow reactors using the karhunen Clove decomposition,Computers&chemical engineering,23(1):109–123,1998.
[12]A.Ghosh,V.R.Kumar,and B.Kulkarni,Parameter estimation in spatially extended systems:The karhunen-leve and galerkin multiple shooting approach,Physical Review E,64(5):056222,2001.
[13]Y.Orlov and J.Bentsman,Adaptive distributed parameter systems identification with enforceable identifiability conditions and reduced-order spatial differentiation,Automatic Control,IEEE Transactions on,45(2):203–216,2000.
[14]J.Baumeister,W.Scondo,M.Demetriou,and I.Rosen,Online parameter estimation for infinite-dimensional dynamical systems,SIAM Journal on Control and Optimization,35(2):678–713,1997.
[15]J.Chang,Identification of variable coefficients for vibrating systems by boundary control and observation,Journal of Control Theory and Applications,6(2):127–132,2008.
[16]C.Wang and D.J.Hill,Learning from neural control,Neural Networks,IEEE Transactions on,17(1):130–146,2006.
[17]C.Wang and D.J.Hill,Deterministic learning and rapid dynamical pattern recognition,Neural Networks,IEEE Transactions on,18(3):617–630,2007.
[18]C.Wang and D.J.Hill,Deterministic learning theory for identification,recognition,and control.Boca Raton:CRC Press,2009.
[19]Y.A.Kuznetsov,Elements of applied bifurcation theory.Berlin:Springer Science&Business Media,2013.
[20]G.Zhou and C.Wang,Deterministic learning from control of nonlinear systems with disturbances,Progress in Natural Science,19(8):1011–1019,2009.