NDLI: Neural-network-based predictive control for nonlinear processes

Content Provider	IEEE Xplore Digital Library
Author	Chi-Huang Lu Yuan-Hai Charng Chi-Ming Liu
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, Hsiuping Institute of Technology, Taichung, Taiwan, R.O.C. (Chi-Huang Lu; Chi-Ming Liu) \|\| Department of Mechanical Engineering, Hsiuping Institute of Technology, Taichung, Taiwan, R.O.C. (Yuan-Hai Charng)
Abstract	This paper presents a design methodology for generalized predictive control (GPC) using recurrent neural networks (RNNs) for a class of nonlinear processes. The discrete-time nonlinear system model using RNN is constructed with an appropriate learning rate, in order to identify the weights in the recurrent neural network model (RNNM). The proposed neural-network-based predictive controller is derived via a generalized predictive performance criterion and an appropriate learning rate for guaranteeing the convergence of the GPC controller. Two examples, including the control of a nonlinear process and the control of a physical variable-frequency oil-cooling machine, are exemplified to demonstrate the effectiveness of the proposed control approach. Both results from numerical simulations and experiments show that the proposed method is capable of controlling a class of nonlinear processes with satisfactory performance under setpoint and load changes.
Starting Page	2858
Ending Page	2863
File Size	398709
Page Count	6
File Format	PDF
ISBN	9781424476428
e-ISBN	9784907764364
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Nonlinear systems Predictive control Temperature control Recurrent neural networks Convergence variable-frequency oil-cooling machine generalized predictive control recurrent neural networks temperature control
Content Type	Text
Resource Type	Article

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