Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/7808
Title: | A model-based PID controller for Hammerstein systems using B-spline neural networks | Authors: | Hong, X. İplikçi, Serdar Chen, S. Warwick, K. |
Keywords: | Adaptive control B-spline neural network De Boor algorithm Hammerstein model Multistep ahead prediction PID controller System identification Algorithms Electric control equipment Identification (control systems) Interpolation Jacobian matrices Neural networks Nonlinear systems Adaptive Control De Boor Algorithm Multi-step PID controllers Proportional control systems |
Publisher: | John Wiley and Sons Ltd | 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. Copyright © 2012 John Wiley & Sons, Ltd. | URI: | https://hdl.handle.net/11499/7808 https://doi.org/10.1002/acs.2293 |
ISSN: | 0890-6327 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Size | Format | |
---|---|---|---|
A model-based PID controller for Hammerstein systems using B-spline.pdf | 379.26 kB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
18
checked on Oct 13, 2024
WEB OF SCIENCETM
Citations
17
checked on Oct 31, 2024
Page view(s)
62
checked on Aug 24, 2024
Download(s)
48
checked on Aug 24, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.