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

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