NDLI: Neural model adaptation and predictive control of a chemical process rig

Content Provider	IEEE Xplore Digital Library
Author	Ding-Li Yu Ding-Wen Yu Gomm, J.B.
Copyright Year	1993
Abstract	An adaptation algorithm for Gaussian radial basis function (RBF) network models is proposed. The model structure is adapted to cope with operating region change, while the weight parameters are updated to model time varying dynamics or uncertainties. The special feature is that the modeling accuracy is maintained during adaptation and, therefore, the control performance will not be degraded when the model structure changes. A localized forgetting method is proposed to deal with nonlinearities in different operating regions, and is implemented with the recursive orthogonal least squares (ROLS) training algorithm. The developed adaptive model is evaluated by real data modeling of a three-input three-output chemical process rig. Online model predictive control (MPC) of the rig is also conducted. Improved tracking performance with the adaptive model is demonstrated in comparison with nonadaptive model-based control and decentralized propotional-integral-differential (PID) control
Sponsorship	IEEE Control Systems Society
Starting Page	828
Ending Page	840
Page Count	13
File Size	626146
File Format	PDF
ISSN	10636536
Volume Number	14
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation model Predictive models Predictive control Chemical processes Uncertainty Degradation Least squares methods Programmable control Adaptive control Three-term control process control Adaptive neural networks localized forgetting nonlinear model predictive control
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering

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