Recursive identification of time-varying non-linear cascade systems with static input and dynamic output non-linearities

The paper deals with the recursive identification of time-varying non-linear dynamic systems using three-block cascade models with non-linear static, linear dynamic and non-linear dynamic blocks. These models are appropriate for systems with both actuator and sensor non-linearities. Multiple application of a decomposition technique provides special expressions for the corresponding non-linear model description that are linear in parameters. A modified recursive least-squares-based algorithm is used for estimation of the time-varying input polynomial and output backlash parameters. Simulation studies show the feasibility of proposed approach to estimate the model parameters and track their changes.

[1]  Fouad Giri,et al.  Combined frequency-prediction error identification approach for Wiener systems with backlash and backlash-inverse operators , 2014, Autom..

[2]  Lincheng Zhou,et al.  Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling , 2013, Nonlinear Dynamics.

[3]  F. Ding,et al.  Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle , 2014 .

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

[5]  Kazys Kazlauskas,et al.  On Intelligent Extraction of an Internal Signal in a Wiener System Consisting of a Linear Block Followed by Hard-Nonlinearity , 2013, Informatica.

[6]  Zygmunt Hasiewicz,et al.  On Nonparametric Identification of Wiener Systems , 2007, IEEE Transactions on Signal Processing.

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

[8]  Jozef Vörös,et al.  Identification of cascade systems with backlash , 2010, Int. J. Control.

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

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

[11]  Xiaoping Xu,et al.  Parameter estimation of piecewise Hammerstein systems , 2014 .

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

[13]  F. Ding,et al.  Newton iterative identification method for an input nonlinear finite impulse response system with moving average noise using the key variables separation technique , 2014, Nonlinear Dynamics.

[14]  Jozef Vörös,et al.  Parameter identification of Wiener systems with multisegment piecewise-linear nonlinearities , 2007, Syst. Control. Lett..

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

[16]  G. Mzyk,et al.  Direct identification of the linear block in Wiener system , 2016 .

[17]  Fouad Giri,et al.  An Analytic Geometry Approach to Wiener System Frequency Identification , 2009, IEEE Transactions on Automatic Control.

[18]  Zygmunt Hasiewicz,et al.  Combined parametric-nonparametric identification of Hammerstein systems , 2004, IEEE Transactions on Automatic Control.

[19]  Er-Wei Bai,et al.  Generalized Wiener system identification: General backlash nonlinearity and finite impulse response linear part , 2014 .

[20]  Feng Ding,et al.  Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle , 2013, Inf. Sci..

[21]  Lennart Ljung,et al.  Theory and Practice of Recursive Identification , 1983 .

[22]  M. Chidambaram,et al.  Computer Control of Processes , 2001 .

[23]  Andrzej Janczak,et al.  Instrumental variables approach to identification of a class of MIMO Wiener systems , 2007 .

[24]  Michael W. Marcellin,et al.  Wavelet Amendment of Polynomial Models in Hammerstein Systems Identification , 2009, IEEE Transactions on Automatic Control.

[25]  Wei Xing Zheng,et al.  A Recursive Local Linear Estimator for Identification of Nonlinear ARX Systems: Asymptotical Convergence and Applications , 2013, IEEE Transactions on Automatic Control.

[26]  Ruifeng Ding,et al.  Gradient-based iterative algorithm for Wiener systems with saturation and dead-zone nonlinearities , 2014 .

[27]  Er-Wei Bai,et al.  Towards identification of Wiener systems with the least amount of a priori information: IIR cases , 2009, Autom..

[28]  Jozef Vörös.  Parametric Identification of Systems with General Backlash , 2012, Informatica.

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

[30]  Zhizhong Mao,et al.  Adaptive control of stochastic Hammerstein systems with dead-zone input non-linearity , 2015 .

[31]  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..

[32]  Jozef Vörös,et al.  Iterative identification of nonlinear dynamic systems with output backlash using three-block cascade models , 2015 .

[33]  Feng Ding,et al.  Least squares based and gradient based iterative identification for Wiener nonlinear systems , 2011, Signal Process..

[34]  Rimantas Pupeikis,et al.  On the Identification of Hammerstein Systems Having Saturation-like Functions with Positive Slopes , 2006, Informatica.