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Please use this identifier to cite or link to this item:
http://ir.hust.edu.tw/dspace/handle/310993100/1603
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| Title: | Discrete-time neural predictive controller design |
| Authors: | Chi-Huang Lu |
| Keywords: | generalized predictive control
recurrent neural network
nonlinear system |
| Date: | 2009-03
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| Issue Date: | 2009-05-24T04:06:26Z
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| Abstract: | This paper presents a design methodology for generalized predictive control (GPC) using recurrent neural network (RNN). A discrete-time mathematical model using RNN is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The neural predictive controller (NPC) is obtained via a generalized predictive performance criterion, and the convergence of the NPC including the adaptive optimal rate (AOR) by the Lyapunov stability theorem is presented. The illustrative process system is used to demonstrate the effectiveness of the proposed strategy. Results from numerical simulations show that the proposed method is capable of controlling nonlinear system with satisfactory performance under setpoint and load changes. |
| Relation: | 修平學報 18, 27-38 |
| Appears in Collections: | [Department of Electrical Engineering & Graduate Institute] Journal
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| 18-03.pdf | | 169Kb | Adobe PDF | 2764 | View/Open |
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