Reconstructed Multi-innovation Gradient Algorithm for the Identification of Sandwich Systems
Inspired by multi-innovation stochastic gradient identification algorithm, a reconstructed multi-innovation stochastic gradient identification algorithm (RMISG) is presented to estimate the parameters of sandwich systems in this paper. Compared with the traditional multi-innovation stochastic gradient identification algorithm, the RMISG is constructed by using the multistep update principle which solves the multi-innovation length problem and improves the performance of the identification algorithm. To decrease the calculation burden of the RMISG, the key-term separation principle is introduced to deal with the identification model of sandwich systems. Finally, simulation example is given to validate the availability of the proposed estimator.
KeywordsParameter estimation Sandwich systems Multi-innovation gradient algorithm Key-term separation principle
This paper is supported by the National Natural Science Foundation of China (No. 61433003, 61273150 and 61321002.), and Shandong Natural Science Foundation of China (ZR2017MF048), Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (2016RCJJ035), Tai’an Science and Technology development program (2017GX0017).
- 1.F. Giri, E.W. Bai, Block-oriented nonlinear system identification, vol. 1 (Springer, 2010)Google Scholar
- 4.J. Vörös, Modeling and identification of nonlinear cascade and sandwich systems with general backlash. Struct. Control Health Monitor. 4(4), 282–290 (2010)Google Scholar
- 11.M. Schoukens, R. Pintelon, Y. Rolain, Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation. Automatica 50(2), 628–634 (2014)Google Scholar
- 12.Y. Tan, R. Dong, R. Li, Recursive identification of sandwich systems with dead zone and application. IEEE Trans. Control Syst. Technol. 17(4), 945–951 (2009)Google Scholar
- 13.Y. Wang, F. Ding, The auxiliary model based hierarchical gradient algorithms and convergence analysis using the filtering technique. Signal Process. 128, 212–221 (2016)Google Scholar
- 14.F. Ding, Coupled-least-squares identification for multivariable systems. IET Control Theory Appl. 7(1), 68–79 (2013)Google Scholar
- 15.Q. Jin, Z. Wang, X. Liu, Auxiliary model-based interval-varying multi-innovation least squares identification for multivariable OE-like systems with scarce measurements. J. Process Control 35, 154–168 (2015)Google Scholar
- 16.V. Cerone, D. Regruto, Bounding the parameters of linear systems with input backlash. IEEE Trans. Auto. Control 52(3), 531–536 (2007)Google Scholar
- 17.Z.Y. Wang, Y. Wang, Z.C. Ji, Filtering based multi-innovation extended stochastic gradient algorithm for Hammerstein nonlinear system modeling. Appl. Math. Model. 39(18), 5724–5732 (2015)Google Scholar