A model-based PID controller for Hammerstein systems using B-spline neural networks

X. Hong, S. Iplikci, S. Chen, Kevin Warwick

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

In this paper, a new model-based proportional–integral–derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches
Original languageEnglish
Pages (from-to)412-428
JournalInternational Journal of Adaptive Control and Signal Processing
Volume28
Issue number3-5
Early online date25 Apr 2012
DOIs
Publication statusPublished - Mar 2014

Bibliographical note

This article is not yet available on the repository

Funder

UK EPSRC and the Council of Higher Education in Turkey.

Keywords

  • B-spline neural network
  • de Boor algorithm
  • Hammerstein model
  • PID controller
  • adaptive control
  • multistep ahead prediction
  • system identification

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