On-line lower-order modeling via neural networks

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...

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Bibliographic Details
Published in:ISA transactions 2003-10, Vol.42 (4), p.577-593
Main Authors: Ho, H.F., Rad, A.B., Wong, Y.K., Lo, W.L.
Format: Article
Language:English
Subjects:
Adaptative systems
Adaptive PID control
Algorithms
Applied sciences
Biotechnology - methods
Computer science
control theory
systems
Computer Simulation
Control theory. Systems
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Feasibility Studies
Feedback
Lower-order modeling
Models, Theoretical
Neural networks
Neural Networks (Computer)
Reproducibility of Results
Sensitivity and Specificity
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Summary: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.
ISSN:0019-0578
1879-2022
DOI:10.1016/S0019-0578(07)60007-X