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

Chen, Mengting and Ding, Feng and Yang, Erfu (2018) Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique. IET Control Theory and Applications, 12 (17). pp. 2380-2389. ISSN 1751-8644 (https://doi.org/10.1049/iet-cta.2018.5254)

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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.

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  Item type:	Article
  ID code:	72119
  Dates:	Date Event 27 November 2018 Published 15 October 2018 Published Online 3 September 2018 Accepted
  Notes:	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering Faculty of Engineering > Design, Manufacture and Engineering Management
  Depositing user:	Pure Administrator
  Date deposited:	21 Apr 2020 12:13
  Last modified:	14 Apr 2024 00:52
  Related URLs:	Scopus publication
  URI:	https://strathprints.strath.ac.uk/id/eprint/72119