Least squares based iterative estimation for multi-input output-error systems using the data filtering
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- 英文论文题名：Least squares based iterative estimation for multi-input output-error systems using the data filtering
- 论文作者：Jiling Ding ; Huibo Chen
- 英文论文作者：Jiling Ding ; Huibo Chen ; Department of Mathematics ; Jining University ; School of Electrical Engineering ; Sanmenxia Polytechnic
- 年：2017
- 作者机构：Department of Mathematics,Jining University;School of Electrical Engineering,Sanmenxia Polytechnic;
- 英文论文关键词：Multivariable system ; System identification ; Least squares ; Iterative algorithm ; Data filtering ; Multi-input system
- 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：O212.1
- 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：190k
- 原文格式：D
- 会议级别：全国
摘要
This paper considers the parameter estimation for multi-input output error autoregressive(OEAR) systems. The main contribution of this paper is to propose iterative algorithms for multi-input OEAR systems using the data filtering technique.Firstly, a least squares iterative(LSI) identification algorithm is given for multi-input OEAR systems as a comparison. Secondly,we transform an OEAR system into two identification models and present a data filtering based least squares iterative(F-LSI)identification algorithm. Finally, an illustrative simulation example is provided to test the proposed algorithms, the simulation results indicate that the F-LSI identification algorithm can produce more accurate parameter estimates than the LSI identification algorithm.
This paper considers the parameter estimation for multi-input output error autoregressive(OEAR) systems. The main contribution of this paper is to propose iterative algorithms for multi-input OEAR systems using the data filtering technique.Firstly, a least squares iterative(LSI) identification algorithm is given for multi-input OEAR systems as a comparison. Secondly,we transform an OEAR system into two identification models and present a data filtering based least squares iterative(F-LSI)identification algorithm. Finally, an illustrative simulation example is provided to test the proposed algorithms, the simulation results indicate that the F-LSI identification algorithm can produce more accurate parameter estimates than the LSI identification algorithm.
This paper considers the parameter estimation for multi-input output error autoregressive(OEAR) systems. The main contribution of this paper is to propose iterative algorithms for multi-input OEAR systems using the data filtering technique.Firstly, a least squares iterative(LSI) identification algorithm is given for multi-input OEAR systems as a comparison. Secondly,we transform an OEAR system into two identification models and present a data filtering based least squares iterative(F-LSI)identification algorithm. Finally, an illustrative simulation example is provided to test the proposed algorithms, the simulation results indicate that the F-LSI identification algorithm can produce more accurate parameter estimates than the LSI identification algorithm.
引文
[1]Y.Shi,J.Huang,B.Yu,Robust tracking control of networked control systems:Application to a networked DC motor,IEEE Transactions on Industrial Electronics 60(12)(2013)5864-5874.
[2]F.Ding,System Identification—New Theory and Methods,Science Press,Beijing,2013.
[3]F.Ding,System Identification—Performances Analysis for Identification Methods,Science Press,Beijing,2014.
[4]F.Ding,System Identification—Multi-Innovation Identification Theory and Methods,Science Press,Beijing,2016.
[5]F.Ding,System Identification—Auxiliary Model Identification Idea and Methods,Science Press,Beijing,2017.
[6]B.Zhang,Z.Z.Mao,Consistent parameter estimation and convergence properties analysis of Hammerstein output-error models,International Journal of Control,Automation,and Systems 13(2)(2015)1-9.
[7]L.Xu,F.Ding,Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling,Circuits,Systems and Signal Processing 36(4)(2017)1735-1753.
[8]M.H.Murillo,A.C.Limache,P.S.Rojas Fredini,L.L.Giovanini,Generalized nonlinear optimal predictive control using iterative state-space trajectories:applications to autonomous flight of UAVs,International Journal of Control,Automation,and Systems 13(2)(2015)361-370.
[9]P.D.Alevizos,An optimal algorithm for computing the nontrivial circuits of a union of iso-oriented rectangles,Information Processing Letters 113(3)(2013)55-59.
[10]R.Venkata Rao,V.D.Kalyankar,G.Waghmare,Parameters optimization of selected casting processes using teachinglearning-based optimization algorithm,Applied Mathematical Modelling 38(2014)5592-5608.
[11]P.Zhou,B.Xiang,J.Fu,T.Y.Chai,Model Approximation of Multiple Delay Transfer Function Models Using MultiplePoint Step Response Fitting,International Journal of Control,Automation,and Systems 10(1)(2012)180-185.
[12]X.L.Luan,Q.Chen,F.Liu,Equivalent Transfer Function based Multi-loop PI Control for High Dimensional Multivariable Systems,International Journal of Control,Automation,and Systems 13(2)(2015)346-352.
[13]E.V.L.Nunes,A.J.Peixotoa,T.R.Oliveira,L.Hsu,Global exact tracking for uncertain MIMO linear systems by output feedback sliding mode control,Journal of the Franklin Institute–Engineering and Applied Mathematics,351(4)(2014)2015-2032.
[14]H.Li,Y.Shi,Robust H-infty filtering for nonlinear stochastic systems with uncertainties and random delays modeled by Markov chains,Automatica 48(1)(2012)159-166.
[15]G.Mercère,L.Bako,Parameterization and identification of multivariable state-space systems:a canonical approach,Automatica 47(8)(2011)1547-1555.
[16]M.S.H?lzel,D.S.Bernstein,A matrix nullspace approach for solving equality-constrained multivariable polynomial leastsquares problems,Automatica 50(12)(2014)3030-3037.
[17]G.Tao,Multivariable adaptive control:A survey,Automatica50(12)(2014)2737-2764
[18]X.H.Wang,F.Ding,Performance analysis of the recursive parameter estimation algorithms for multivariable BoxJenkins systems,Journal of the Franklin Institute 351(10)(2014)4749-4764.
[19]D.Q.Wang,Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models,Applied Mathematics Letters 57(2016)13-19.
[20]D.Q.Wang,F.Ding,Parameter estimation algorithms for multivariable Hammerstein CARMA systems,Information Sciences 355(2016)237-248.
[21]D.Q.Wang,L.Mao,F.Ding,Recasted models based hierarchical extended stochastic gradient method for MIMO nonlinear systems,IET Control Theory and Applications 11(4)(2017)476-485.
[22]S.Houda,K.Samira,A recursive parametric estimation algorithm of multivariable nonlinear systems described by Hammerstein mathematical models,Applied Mathematical Modelling 39(16)(2015)4951-4962.
[23]A.Wills,B.Ninness,On gradient-based search for multivariable system estimates,IEEE Transaction on Automatic Control 53(1)(2008)298-306.
[24]D.Q.Wang,W.Zhang,Improved least squares identification algorithm for multivariable Hammerstein systems,Journal of the Franklin Institute 352(11)(2015)5292-5307.
[25]Q.B.Jin,Z.Wang,X.P.Liu,Auxiliary model-based intervalvarying multi-innovation least squares identification for multivariable OE-like systems with scarce measurements,Journal of Process Control 35(11)(2015)154-168.
[26]J.X.Ma,W.L.Xiong,F.Ding,Iterative identification algorithms for input nonlinear output error autoregressive systems,International Journal of Control,Automation,and Systems14(1)(2016)140-147.
[27]F.Ding,F.F.Wang,L.Xu,M.H.Wu,Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering,Journal of the Franklin Institute 354(3)(2017)1321-1339.
[28]L.Xu,The damping iterative parameter identification method for dynamical systems based on the sine signal measurement,Signal Processing 120(2016)660-667.
[29]X.G.Liu,J.Lu,Least squares based iterative identification for a class of multirate systems,Automatica 46(3)(2010)549-554.
[30]E.W.Bai,K.Li,Convergence of the iterative algorithm for a general Hammerstein system identification,Automatica 46(11)(2010)1891-1896.
[31]M.Dehghan,M.Hajarian,Solving Coupled Matrix Equations over Generalized Bisymmetric Matrices,International Journal of Control,Automation,and Systems 10(5)(2012)905-912.
[32]F.Ding,L.Xu,Q.M.Zhu,Performance analysis of the generalised projection identification for time-varying systems,IET Control Theory and Applications 10(18)(2016)2506-2514.
[33]F.Ding,F.F.Wang,L.Xu,T.Hayat,A.Alsaedi,Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique,IET Control Theory and Applications 11(3)(2017)390-400.
[34]F.Ding,Y.J.Wang,J.Ding,Recursive least squares parameter identification for systems with colored noise using the filtering technique and the auxiliary model,Digital Signal Processing37(2015)100-108.
[35]J.Huang,Y.Shi,H.N.Huang,Z.Li,l-2–l-infinity filtering for multirate nonlinear sampled-data systems using T-S fuzzy models,Digital Signal Processing 23(1)(2013)418-426.
[36]M.Basin,P.Shi,et al.,Joint state filtering and parameter estimation for linear stochastic time-delay systems,Signal Processing 91(4)(2011)782-792.
[37]F.Ding,X.H.Wang,L.Mao,L.Xu,Joint state and multiinnovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering,Digital Signal Processing 62(2017)211-223.
[38]Y.Shi,H.Fang,Kalman filter based identification for systems with randomly missing measurements in a network environment,International Journal of Control 83(3)(2010)538-551.
[39]M.Scarpiniti,D.Comminiello,R.Parisi,A.Uncini,Nonlinear system identification using IIR spline adaptive filters,Signal Processing 108(2015)30-35.
[2]F.Ding,System Identification—New Theory and Methods,Science Press,Beijing,2013.
[3]F.Ding,System Identification—Performances Analysis for Identification Methods,Science Press,Beijing,2014.
[4]F.Ding,System Identification—Multi-Innovation Identification Theory and Methods,Science Press,Beijing,2016.
[5]F.Ding,System Identification—Auxiliary Model Identification Idea and Methods,Science Press,Beijing,2017.
[6]B.Zhang,Z.Z.Mao,Consistent parameter estimation and convergence properties analysis of Hammerstein output-error models,International Journal of Control,Automation,and Systems 13(2)(2015)1-9.
[7]L.Xu,F.Ding,Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling,Circuits,Systems and Signal Processing 36(4)(2017)1735-1753.
[8]M.H.Murillo,A.C.Limache,P.S.Rojas Fredini,L.L.Giovanini,Generalized nonlinear optimal predictive control using iterative state-space trajectories:applications to autonomous flight of UAVs,International Journal of Control,Automation,and Systems 13(2)(2015)361-370.
[9]P.D.Alevizos,An optimal algorithm for computing the nontrivial circuits of a union of iso-oriented rectangles,Information Processing Letters 113(3)(2013)55-59.
[10]R.Venkata Rao,V.D.Kalyankar,G.Waghmare,Parameters optimization of selected casting processes using teachinglearning-based optimization algorithm,Applied Mathematical Modelling 38(2014)5592-5608.
[11]P.Zhou,B.Xiang,J.Fu,T.Y.Chai,Model Approximation of Multiple Delay Transfer Function Models Using MultiplePoint Step Response Fitting,International Journal of Control,Automation,and Systems 10(1)(2012)180-185.
[12]X.L.Luan,Q.Chen,F.Liu,Equivalent Transfer Function based Multi-loop PI Control for High Dimensional Multivariable Systems,International Journal of Control,Automation,and Systems 13(2)(2015)346-352.
[13]E.V.L.Nunes,A.J.Peixotoa,T.R.Oliveira,L.Hsu,Global exact tracking for uncertain MIMO linear systems by output feedback sliding mode control,Journal of the Franklin Institute–Engineering and Applied Mathematics,351(4)(2014)2015-2032.
[14]H.Li,Y.Shi,Robust H-infty filtering for nonlinear stochastic systems with uncertainties and random delays modeled by Markov chains,Automatica 48(1)(2012)159-166.
[15]G.Mercère,L.Bako,Parameterization and identification of multivariable state-space systems:a canonical approach,Automatica 47(8)(2011)1547-1555.
[16]M.S.H?lzel,D.S.Bernstein,A matrix nullspace approach for solving equality-constrained multivariable polynomial leastsquares problems,Automatica 50(12)(2014)3030-3037.
[17]G.Tao,Multivariable adaptive control:A survey,Automatica50(12)(2014)2737-2764
[18]X.H.Wang,F.Ding,Performance analysis of the recursive parameter estimation algorithms for multivariable BoxJenkins systems,Journal of the Franklin Institute 351(10)(2014)4749-4764.
[19]D.Q.Wang,Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models,Applied Mathematics Letters 57(2016)13-19.
[20]D.Q.Wang,F.Ding,Parameter estimation algorithms for multivariable Hammerstein CARMA systems,Information Sciences 355(2016)237-248.
[21]D.Q.Wang,L.Mao,F.Ding,Recasted models based hierarchical extended stochastic gradient method for MIMO nonlinear systems,IET Control Theory and Applications 11(4)(2017)476-485.
[22]S.Houda,K.Samira,A recursive parametric estimation algorithm of multivariable nonlinear systems described by Hammerstein mathematical models,Applied Mathematical Modelling 39(16)(2015)4951-4962.
[23]A.Wills,B.Ninness,On gradient-based search for multivariable system estimates,IEEE Transaction on Automatic Control 53(1)(2008)298-306.
[24]D.Q.Wang,W.Zhang,Improved least squares identification algorithm for multivariable Hammerstein systems,Journal of the Franklin Institute 352(11)(2015)5292-5307.
[25]Q.B.Jin,Z.Wang,X.P.Liu,Auxiliary model-based intervalvarying multi-innovation least squares identification for multivariable OE-like systems with scarce measurements,Journal of Process Control 35(11)(2015)154-168.
[26]J.X.Ma,W.L.Xiong,F.Ding,Iterative identification algorithms for input nonlinear output error autoregressive systems,International Journal of Control,Automation,and Systems14(1)(2016)140-147.
[27]F.Ding,F.F.Wang,L.Xu,M.H.Wu,Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering,Journal of the Franklin Institute 354(3)(2017)1321-1339.
[28]L.Xu,The damping iterative parameter identification method for dynamical systems based on the sine signal measurement,Signal Processing 120(2016)660-667.
[29]X.G.Liu,J.Lu,Least squares based iterative identification for a class of multirate systems,Automatica 46(3)(2010)549-554.
[30]E.W.Bai,K.Li,Convergence of the iterative algorithm for a general Hammerstein system identification,Automatica 46(11)(2010)1891-1896.
[31]M.Dehghan,M.Hajarian,Solving Coupled Matrix Equations over Generalized Bisymmetric Matrices,International Journal of Control,Automation,and Systems 10(5)(2012)905-912.
[32]F.Ding,L.Xu,Q.M.Zhu,Performance analysis of the generalised projection identification for time-varying systems,IET Control Theory and Applications 10(18)(2016)2506-2514.
[33]F.Ding,F.F.Wang,L.Xu,T.Hayat,A.Alsaedi,Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique,IET Control Theory and Applications 11(3)(2017)390-400.
[34]F.Ding,Y.J.Wang,J.Ding,Recursive least squares parameter identification for systems with colored noise using the filtering technique and the auxiliary model,Digital Signal Processing37(2015)100-108.
[35]J.Huang,Y.Shi,H.N.Huang,Z.Li,l-2–l-infinity filtering for multirate nonlinear sampled-data systems using T-S fuzzy models,Digital Signal Processing 23(1)(2013)418-426.
[36]M.Basin,P.Shi,et al.,Joint state filtering and parameter estimation for linear stochastic time-delay systems,Signal Processing 91(4)(2011)782-792.
[37]F.Ding,X.H.Wang,L.Mao,L.Xu,Joint state and multiinnovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering,Digital Signal Processing 62(2017)211-223.
[38]Y.Shi,H.Fang,Kalman filter based identification for systems with randomly missing measurements in a network environment,International Journal of Control 83(3)(2010)538-551.
[39]M.Scarpiniti,D.Comminiello,R.Parisi,A.Uncini,Nonlinear system identification using IIR spline adaptive filters,Signal Processing 108(2015)30-35.