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請使用永久網址來引用或連結此文件:
http://ir.hust.edu.tw/dspace/handle/310993100/1603
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| 題名: | Discrete-time neural predictive controller design |
| 作者: | Chi-Huang Lu |
| 關鍵詞: | generalized predictive control
recurrent neural network
nonlinear system |
| 日期: | 2009-03
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| 上傳時間: | 2009-05-24T04:06:26Z
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| 摘要: | 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. |
| 關聯: | 修平學報 18, 27-38 |
| 顯示於類別: | [電機工程系(含碩士班)] 期刊論文
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