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https://hdl.handle.net/11499/8508
Title: | B-spline neural networks based PID controller for Hammerstein systems | Authors: | Hong, X. İplikçi, Serdar Chen, S. Warwick, K. |
Keywords: | Hammerstein model PID controller system identification B-spline neural network Control signal Correction terms De Boor Algorithm Hammerstein system Input/output datum Jacobians Multi-step Nonlinear static function Numerical example PID controllers PID tuning Recursions Electric control equipment Identification (control systems) Intelligent computing Neural networks Nonlinear systems Proportional control systems |
Abstract: | A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches. © 2012 Springer-Verlag. | URI: | https://hdl.handle.net/11499/8508 https://doi.org/10.1007/978-3-642-31837-5_6 |
ISBN: | 18650929 (ISSN) 9783642318368 |
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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