Decomposition based least squares iterative estimation algorithm for two-input single-output output error systems

Abstract This paper develops a decomposition based least squares iterative identification algorithm for two-input single-output (TISO) systems. The basic idea is to decompose a TISO system into two subsystems and then to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm has less computational load. The simulation results indicate that the proposed algorithm is effective.

[1]  Fei Liu,et al.  H∞ Control for Discrete-Time Markov Jump Systems With Uncertain Transition Probabilities , 2013, IEEE Transactions on Automatic Control.

[2]  Feng Ding,et al.  Hierarchical estimation algorithms for multivariable systems using measurement information , 2014, Inf. Sci..

[3]  J. Chu,et al.  Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle , 2013 .

[4]  Feng Ding,et al.  Combined state and least squares parameter estimation algorithms for dynamic systems , 2014 .

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

[6]  Yong Zhang,et al.  Bias compensation methods for stochastic systems with colored noise , 2011 .

[7]  Feng Ding,et al.  State filtering and parameter estimation for state space systems with scarce measurements , 2014, Signal Process..

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

[9]  Mehdi Dehghan,et al.  Iterative algorithms for the generalized centro‐symmetric and central anti‐symmetric solutions of general coupled matrix equations , 2012 .

[10]  Feng Ding,et al.  Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model , 2015, Digit. Signal Process..

[11]  Y. Liu,et al.  Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems , 2012, J. Syst. Control. Eng..

[12]  Maryam Dehestani,et al.  Solving the nonlinear equations for one-dimensional nano-sized model including Rydberg and Varshni potentials and Casimir force using the decomposition method , 2013 .

[13]  Tao Tang,et al.  Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems , 2014, Appl. Math. Lett..

[14]  Lennart Ljung,et al.  Implementation of algorithms for tuning parameters in regularized least squares problems in system identification , 2013, Autom..

[15]  Feng Ding,et al.  Hierarchical Least Squares Identification for Linear SISO Systems With Dual-Rate Sampled-Data , 2011, IEEE Transactions on Automatic Control.

[16]  D. Wang.  Brief paper: Lleast squares-based recursive and iterative estimation for output error moving average systems using data filtering , 2011 .

[17]  Fei Liu,et al.  Stabilization of Networked Control Systems With Random Delays , 2011, IEEE Transactions on Industrial Electronics.

[18]  M. Dehghan,et al.  Analysis of an iterative algorithm to solve the generalized coupled Sylvester matrix equations , 2011 .

[19]  F. Ding.  Coupled-least-squares identification for multivariable systems , 2013 .

[20]  Feng Ding,et al.  States based iterative parameter estimation for a state space model with multi-state delays using decomposition , 2015, Signal Process..

[21]  Robert R. Bitmead,et al.  Instability detection of ARMA systems based on AR system identification , 2011, Syst. Control. Lett..

[22]  J. Voros.  Modeling and parameter identification of systems with multisegment piecewise-linear characteristics , 2002 .

[23]  Yang Shi,et al.  Output Feedback Stabilization of Networked Control Systems With Random Delays Modeled by Markov Chains , 2009, IEEE Transactions on Automatic Control.

[24]  Jie Ding,et al.  Modified Subspace Identification for Periodically Non-uniformly Sampled Systems by Using the Lifting Technique , 2013, Circuits, Systems, and Signal Processing.

[25]  Baolin Liu,et al.  Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises , 2013, Circuits, Systems, and Signal Processing.

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

[27]  K. Narendra,et al.  An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .

[28]  F. Ding.  Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling , 2013 .

[29]  Feng Ding,et al.  State filtering and parameter estimation for linear systems with d-step state-delay , 2014, IET Signal Process..

[30]  Jing Lu,et al.  Least squares based iterative identification for a class of multirate systems , 2010, Autom..

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

[32]  Ji Huang,et al.  Robust Tracking Control of Networked Control Systems: Application to a Networked DC Motor , 2013, IEEE Transactions on Industrial Electronics.

[33]  Tao Tang,et al.  Several gradient-based iterative estimation algorithms for a class of nonlinear systems using the filtering technique , 2014 .

[34]  Yong Zhang,et al.  Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods , 2011, Math. Comput. Model..

[35]  Hu Yuanbiao.  Iterative and recursive least squares estimation algorithms for moving average systems , 2013 .

[36]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[37]  Huiping Li,et al.  Robust Distributed Model Predictive Control of Constrained Continuous-Time Nonlinear Systems: A Robustness Constraint Approach , 2014, IEEE Transactions on Automatic Control.

[38]  Wenge Zhang,et al.  Decomposition based least squares iterative estimation algorithm for output error moving average systems , 2014 .

[39]  Fei Liu,et al.  H∞ Filtering for Discrete-Time Systems With Stochastic Incomplete Measurement and Mixed Delays , 2012, IEEE Trans. Ind. Electron..

[40]  Huiping Li,et al.  Event-triggered robust model predictive control of continuous-time nonlinear systems , 2014, Autom..

[41]  Feng Ding,et al.  Identification methods for Hammerstein nonlinear systems , 2011, Digit. Signal Process..

[42]  Huiping Li,et al.  Distributed receding horizon control of large-scale nonlinear systems: Handling communication delays and disturbances , 2014, Autom..

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

[44]  Baolin Liu,et al.  A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems , 2014, Appl. Math. Comput..

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

[46]  Mehdi Dehghan,et al.  SSHI methods for solving general linear matrix equations , 2011 .

[47]  Yong Zhang,et al.  Computers and Mathematics with Applications , 2022 .

[48]  Bo Yu,et al.  Robust mixed H2/H∞ control of networked control systems with random time delays in both forward and backward communication links , 2011, Autom..

[49]  Feng Ding,et al.  Self-tuning control based on multi-innovation stochastic gradient parameter estimation , 2009, Syst. Control. Lett..