Data filtering based forgetting factor stochastic gradient algorithm for Hammerstein systems with saturation and preload nonlinearities

Abstract This paper considers the parameter estimation problem for Hammerstein systems with saturation and preload nonlinearities. Based on the key term separation technique, the output of the system is expressed as a linear combination of all the system parameters. By introducing the forgetting factors and using the data filtering technique, a data filtering based forgetting factor stochastic gradient (F-FF-SG) algorithm is presented. The simulation examples illustrate that the F-FF-SG algorithm has faster convergence rates and better parameter estimation accuracies than the stochastic gradient algorithm and the data filtering based stochastic gradient algorithm.

[1]  Feng Ding,et al.  Recursive Parameter Estimation Algorithms and Convergence for a Class of Nonlinear Systems with Colored Noise , 2016, Circuits Syst. Signal Process..

[2]  Fei Liu,et al.  Fast Kalman-Like Optimal Unbiased FIR Filtering With Applications , 2016, IEEE Transactions on Signal Processing.

[3]  Xinggao Liu,et al.  Recursive maximum likelihood method for the identification of Hammerstein ARMAX system , 2016 .

[4]  Ling Xu,et al.  A proportional differential control method for a time-delay system using the Taylor expansion approximation , 2014, Appl. Math. Comput..

[5]  Feng Ding,et al.  Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model , 2016, Autom..

[6]  Feng Ding,et al.  An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering , 2016, J. Frankl. Inst..

[7]  F. Ding,et al.  Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique , 2015 .

[8]  Huazhen Fang,et al.  Kalman filter-based identification for systems with randomly missing measurements in a network environment , 2010, Int. J. Control.

[9]  Ling Xu,et al.  Application of the Newton iteration algorithm to the parameter estimation for dynamical systems , 2015, J. Comput. Appl. Math..

[10]  Feng Ding,et al.  Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering , 2016 .

[11]  Xianqiang Yang,et al.  EM algorithm-based identification of a class of nonlinear Wiener systems with missing output data , 2015 .

[12]  Jozef Vörös,et al.  Modeling and identification of systems with backlash , 2010, Autom..

[13]  Feng Ding,et al.  Parameter estimation algorithms for multivariable Hammerstein CARMA systems , 2016, Inf. Sci..

[14]  Fei Liu,et al.  Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions , 2017, IEEE Transactions on Automatic Control.

[15]  Feng Ding,et al.  Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition , 2016, J. Comput. Appl. Math..

[16]  Hao Wu,et al.  An adaptive confidence limit for periodic non-steady conditions fault detection , 2016 .

[17]  Feng Ding,et al.  Recursive Least Squares and Multi-innovation Stochastic Gradient Parameter Estimation Methods for Signal Modeling , 2017, Circuits Syst. Signal Process..

[18]  Jiandong Wang,et al.  Identification of extended Hammerstein systems with hysteresis-type input nonlinearities described by Preisach model , 2014, Nonlinear Dynamics.

[19]  Feng Ding,et al.  Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle , 2014, Appl. Math. Comput..

[20]  Ai-Guo Wu,et al.  Bias compensation-based recursive least-squares estimation with forgetting factors for output error moving average systems , 2014, IET Signal Process..

[21]  Ling Xu,et al.  Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration , 2015 .

[22]  Feng Ding,et al.  Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition , 2016, Circuits Syst. Signal Process..

[23]  Jie Ding,et al.  Auxiliary model based parameter estimation for dual-rate output error systems with colored noise ☆ , 2013 .

[24]  Feng Ding,et al.  Performance bounds of forgetting factor least-squares algorithms for time-varying systems with finite measurement data , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[25]  Cheng Wang,et al.  Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory , 2015, J. Frankl. Inst..

[26]  Vojislav Z. Filipovic,et al.  Consistency of the robust recursive Hammerstein model identification algorithm , 2015, J. Frankl. Inst..

[27]  F. Ding,et al.  Filtering-based iterative identification for multivariable systems , 2016 .

[28]  Huiping Li,et al.  Distributed receding horizon control of constrained nonlinear vehicle formations with guaranteed γ-gain stability , 2016, Autom..

[29]  Yide Wang,et al.  Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter. , 2016, ISA transactions.

[30]  Ling Xu,et al.  The damping iterative parameter identification method for dynamical systems based on the sine signal measurement , 2016, Signal Process..

[31]  F. Ding,et al.  Convergence of the recursive identification algorithms for multivariate pseudo‐linear regressive systems , 2016 .

[32]  Jian Pan,et al.  Image noise smoothing using a modified Kalman filter , 2016, Neurocomputing.

[33]  Sirish L. Shah,et al.  Recursive constrained state estimation using modified extended Kalman filter , 2014, Comput. Chem. Eng..

[34]  Er-Wei Bai,et al.  Identification of linear systems with hard input nonlinearities of known structure , 2002, Autom..

[35]  Biao Huang,et al.  Parameter estimation in batch process using EM algorithm with particle filter , 2013, Comput. Chem. Eng..

[36]  Huiping Li,et al.  On Neighbor Information Utilization in Distributed Receding Horizon Control for Consensus-Seeking , 2016, IEEE Transactions on Cybernetics.

[37]  Hooshang Jazayeri-Rad,et al.  Applying a dual extended Kalman filter for the nonlinear state and parameter estimations of a continuous stirred tank reactor , 2011, Comput. Chem. Eng..

[38]  Feng Ding,et al.  The auxiliary model based hierarchical gradient algorithms and convergence analysis using the filtering technique , 2016, Signal Process..

[39]  Ruifeng Ding,et al.  Gradient based estimation algorithm for Hammerstein systems with saturation and dead-zone nonlinearities , 2012 .

[40]  Fouad Giri,et al.  Persistent Excitation by Deterministic Signals for Subspace Parametric Identification of MISO Hammerstein Systems , 2016, IEEE Transactions on Automatic Control.

[41]  Jozef Vörös,et al.  Iterative algorithm for parameter identification of Hammerstein systems with two-segment nonlinearities , 1999, IEEE Trans. Autom. Control..

[42]  Jozef Vörös,et al.  Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models , 2014, J. Frankl. Inst..

[43]  Kiyoshi Nishiyama,et al.  A new approach to introducing a forgetting factor into the normalized least mean squares algorithm , 2015, Signal Process..

[44]  Jozef Vörös,et al.  Recursive identification of Hammerstein systems with discontinuous nonlinearities containing dead-zones , 2003, IEEE Trans. Autom. Control..

[45]  Yan Ji,et al.  Unified Synchronization Criteria for Hybrid Switching-Impulsive Dynamical Networks , 2015, Circuits Syst. Signal Process..

[46]  Fouad Giri,et al.  Identification of Hammerstein systems in presence of hysteresis-backlash and hysteresis-relay nonlinearities , 2008, Autom..

[47]  Wei Zhang,et al.  Improved least squares identification algorithm for multivariable Hammerstein systems , 2015, J. Frankl. Inst..

[48]  Dongqing Wang,et al.  Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models , 2016, Appl. Math. Lett..

[49]  Feng Ding,et al.  The recursive least squares identification algorithm for a class of Wiener nonlinear systems , 2016, J. Frankl. Inst..