Decomposition-based recursive least-squares parameter estimation algorithm for Wiener-Hammerstein systems with dead-zone nonlinearity

ABSTRACT In this paper, a decomposition-based recursive least-squares algorithm is proposed for the parameter estimation of Wiener-Hammerstein systems with dead-zone. Based on a smooth parameterisation of the dead-zone nonlinearity, the Wiener-Hammerstein systems with dead-zone can be transformed into a particular model where the parameter vector involves the least number of parameters needed for the identification model description by using the key-term separation principle. On the basis of the particular model, the hierarchical identification principle is presented to decompose the particular model into two identification subsystems, whose parameters are estimated by using a recursive least squares and the auxiliary model method. Furthermore, the convergence analysis of the proposed algorithm ensures that the estimated parameters convergence to their true values. Compared with recursive least squares algorithm and multi-innovation least-squares, the proposed algorithm can avoid the redundant parameters estimation, and meanwhile reduce the computational burden. Numerical examples and experiment are carried out to illustrate the validity of the proposed algorithm.

[1]  Feng Ding,et al.  Combined parameter and output estimation of dual-rate systems using an auxiliary model , 2004, Autom..

[2]  Feng Ding,et al.  Hierarchical Least Squares Estimation Algorithm for Hammerstein–Wiener Systems , 2012, IEEE Signal Processing Letters.

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

[4]  Marcelo A. Savi,et al.  Sliding mode control with adaptive fuzzy dead-zone compensation for uncertain chaotic systems , 2012 .

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

[6]  Ziyun Wang,et al.  Hierarchical least squares algorithms for nonlinear feedback system modeling , 2016, J. Frankl. Inst..

[7]  Jie Chen,et al.  Identifier-Based Adaptive Robust Control for Servomechanisms With Improved Transient Performance , 2010, IEEE Transactions on Industrial Electronics.

[8]  Yu Guo,et al.  Adaptive Prescribed Performance Motion Control of Servo Mechanisms with Friction Compensation , 2014, IEEE Transactions on Industrial Electronics.

[9]  Jing Chen,et al.  Identification of Hammerstein systems with continuous nonlinearity , 2015, Inf. Process. Lett..

[10]  Feng Ding,et al.  Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle , 2017, IET Signal Process..

[11]  Er-Wei Bai,et al.  Identification of a modified Wiener-Hammerstein system and its application in electrically stimulated paralyzed skeletal muscle modeling , 2009, Autom..

[12]  O. Garcia,et al.  Hybrid Wiener-Hammerstein Structure for Grey-Box Modeling of DC-DC Converters , 2009, 2009 Twenty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition.

[13]  Feng Ding,et al.  Highly Efficient Identification Methods for Dual-Rate Hammerstein Systems , 2015, IEEE Transactions on Control Systems Technology.

[14]  Gang Tao,et al.  Adaptive Control of Plants with Unknown Dead-zones , 1992, 1992 American Control Conference.

[15]  X. Ren,et al.  Non-iterative identification and model following control of hammerstein systems with asymmetric dead-zone non-linearities , 2012 .

[16]  Feng Ding,et al.  An efficient hierarchical identification method for general dual-rate sampled-data systems , 2014, Autom..

[17]  Huanqing Wang,et al.  Adaptive fuzzy control for pure-feedback stochastic nonlinear systems with unknown dead-zone input , 2014, Int. J. Syst. Sci..

[18]  F. Giri,et al.  FREQUENCY IDENTIFICATION OF HAMMERSTEIN-WIENER SYSTEMS WITH PIECEWISE AFFINE INPUT NONLINEARITY , 2014 .

[19]  Jing Na,et al.  Adaptive neural dynamic surface control for servo systems with unknown dead-zone , 2011 .

[20]  Alexandre Seuret,et al.  Design of a Pressure Control System With Dead Band and Time Delay , 2007, IEEE Transactions on Control Systems Technology.

[21]  F. Ding.  Two-stage least squares based iterative estimation algorithm for CARARMA system modeling ☆ , 2013 .

[22]  P. X. Liu,et al.  Multiinnovation Least-Squares Identification for System Modeling , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Ruoyu Li,et al.  Recursive Identification of Sandwich Systems With Dead Zone and Application , 2009, IEEE Transactions on Control Systems Technology.

[24]  Feng Yu,et al.  Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity , 2013 .

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

[26]  Feng Ding,et al.  Two-stage parameter estimation algorithms for Box-Jenkins systems , 2013, IET Signal Process..

[27]  Yonghong Tan,et al.  Soft measurement of states of sandwich system with dead zone and its application , 2016 .

[28]  F. Ding,et al.  An auxiliary model based on a recursive least-squares parameter estimation algorithm for non-uniformly sampled multirate systems , 2009 .

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

[31]  Maryam Dehghani,et al.  Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm. , 2014, ISA transactions.

[32]  Jozef Vörös.  Parameter identification of Wiener systems with discontinuous nonlinearities , 2001, Syst. Control. Lett..

[33]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[34]  Feng Ding,et al.  Hierarchical gradient based and hierarchical least squares based iterative parameter identification for CARARMA systems , 2014, Signal Process..

[35]  Fouad Giri,et al.  Hammerstein Systems Identification in Presence of Hard Nonlinearities of Preload and Dead-Zone Type , 2009, IEEE Transactions on Automatic Control.

[36]  Yanjun Liu,et al.  Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model , 2009, Appl. Math. Comput..

[37]  Feng Ding,et al.  Auxiliary model-based least-squares identification methods for Hammerstein output-error systems , 2007, Syst. Control. Lett..

[38]  T. Pan,et al.  Parameter estimation for Non-uniformly Sampled Wiener Systems with Dead-zone Nonlinearities , 2015 .

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

[40]  Yangqiu Xie,et al.  Identification of sandwich systems with a dead zone using combinational input signals , 2011 .

[41]  Feng Ding,et al.  A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems , 2015, Digit. Signal Process..

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

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

[44]  Pedro Alou Cervera,et al.  Hybrid Wiener-Hammerstein Structure for Grey-Box Modeling of DC-DC Converters , 2009, APEC 2009.

[45]  Junhong Li,et al.  Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration , 2013, Appl. Math. Lett..

[46]  Xingjian Wang,et al.  Adaptive fuzzy control with smooth inverse for nonlinear systems preceded by non-symmetric dead-zone , 2016, Int. J. Syst. Sci..