Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34728
Title: On-line lower-order modeling via neural networks
Authors: Ho, HF
Rad, AB
Wong, YK 
Lo, WL
Keywords: Lower-order modeling
Neural networks
Adaptive PID control
Issue Date: 2003
Publisher: Elsevier
Source: ISA transactions, 2003, v. 42, no. 4, p. 577-593 How to cite?
Journal: ISA transactions 
Abstract: This paper presents a novel method to determine the parameters of a first-order plus dead-time model using neural networks. The outputs of the neural networks are the gain, dominant time constant, and apparent time delay. By combining this algorithm with a conventional PI or PID controller, we also present an adaptive controller which requires very little a priori knowledge about the plant under control. The simplicity of the scheme for real-time control provides a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation and experimental results demonstrate the feasibility and adaptive property of the proposed scheme.
URI: http://hdl.handle.net/10397/34728
ISSN: 0019-0578
DOI: 10.1016/S0019-0578(07)60007-X
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