Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique

Mengting Chen, Feng Ding, Erfu Yang

Research output: Contribution to journalArticle

5 Citations (Scopus)
4 Downloads (Pure)

Abstract

The parameter estimation issues of a block-oriented non-linear system that is bilinear in the parameters are studied, i.e. the bilinear-in-parameter system. Using the model decomposition technique, the bilinear-in-parameter model is decomposed into two fictitious submodels: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear dynamic one and the noise model. Then a gradient-based iterative algorithm is proposed to estimate all the unknown parameters by formulating and minimising two criterion functions. The stochastic gradient algorithms are provided for comparison. The simulation results indicate that the proposed iterative algorithm can give higher parameter estimation accuracy than the stochastic gradient algorithms.

Original languageEnglish
Pages (from-to)2380-2389
Number of pages10
JournalIET Control Theory and Applications
Volume12
Issue number17
Early online date15 Oct 2018
DOIs
Publication statusPublished - 27 Nov 2018

Keywords

  • bilinear systems
  • model composition technique
  • gradient methods
  • nonlinear control system

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