Modelling and identification for non-uniformly periodically sampled-data systems

The authors state the non-uniformly periodically sampling pattern and derives the state-space models of non-uniformly sampled-data systems with coloured noises, and further obtains the corresponding transfer function models. Difficulties of identification are that there exist unknown inner variables and unmeasurable noise terms in the information vectors. By means of the auxiliary model method, an auxiliary model based multi-innovation generalised extended stochastic gradient (SG) algorithm is presented by expanding the scalar innovation to the innovation vector and introducing the innovation length. The proposed algorithm provides higher parameter estimation accuracy and faster convergence rate than the SG algorithm due to repeatedly using the system innovation.

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

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

[3]  R. Gudi,et al.  Multirate state and parameter estimation in an antibiotic fermentation with delayed measurements , 1994, Biotechnology and bioengineering.

[4]  Dongguang Li,et al.  Identification of fast-rate models from multirate data , 2001 .

[5]  Roberto Sanchis,et al.  Recursive identification under scarce measurements - convergence analysis , 2002, Autom..

[6]  Torsten Söderström,et al.  Identification of continuous-time AR processes from unevenly sampled data , 2002, Autom..

[7]  Sirish L. Shah,et al.  Generalized predictive control for non-uniformly sampled systems , 2002 .

[8]  Dongguang Li,et al.  Application of dual-rate modeling to CCR octane quality inferential control , 2001, IEEE Trans. Control. Syst. Technol..

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

[10]  Tongwen Chen,et al.  Interpretations of and options in dual-rate predictive control , 2005 .

[11]  Feng Ding,et al.  Hierarchical identification of lifted state-space models for general dual-rate systems , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  M. Embiruçu,et al.  Multirate multivariable generalized predictive control and its application to a slurry reactor for ethylene polymerization , 2006 .

[13]  Sirish L. Shah,et al.  Identification of chemical processes with irregular output sampling , 2006 .

[14]  Torsten Söderström,et al.  Identification of Continuous-Time ARX Models From Irregularly Sampled Data , 2007, IEEE Transactions on Automatic Control.

[15]  Feng Ding,et al.  Adaptive Digital Control of Hammerstein Nonlinear Systems with Limited Output Sampling , 2007, SIAM J. Control. Optim..

[16]  Feng Ding,et al.  Performance analysis of multi-innovation gradient type identification methods , 2007, Autom..

[17]  R. Gudi,et al.  Nonlinear predictive control of irregularly sampled multirate systems using blackbox observers , 2007 .

[18]  Sirish L. Shah,et al.  Kalman filters in non-uniformly sampled multirate systems: For FDI and beyond , 2008, Autom..

[19]  Yucai Zhu,et al.  System identification using slow and irregular output samples , 2009 .

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

[21]  Feng Ding,et al.  Multi-innovation stochastic gradient algorithms for multi-input multi-output systems , 2009, Digit. Signal Process..

[22]  Feng Ding,et al.  Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises , 2009, Signal Process..

[23]  Feng Ding,et al.  Computers and Mathematics with Applications Identification for Multirate Multi-input Systems Using the Multi-innovation Identification Theory , 2022 .

[24]  Feng Ding,et al.  Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems , 2009, Autom..