Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems
碩士 === 國立交通大學 === 電機與控制工程系 === 90 === In this work, we propose an integrated approach to fuzzy modeling and optimal fuzzy control for unknown nonlinear systems. We first obtain the Takagi-Sugeno (T-S) fuzzy model of the nonlinear plant by linear self-constructing neural fuzzy inference network (line...
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2002
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ndltd-TW-090NCTU05910832015-10-13T10:08:07Z http://ndltd.ncl.edu.tw/handle/27089656634052171071 Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems 類神經網路為基礎之非線性系統的最佳化模糊控制器設計 林玟叡 碩士 國立交通大學 電機與控制工程系 90 In this work, we propose an integrated approach to fuzzy modeling and optimal fuzzy control for unknown nonlinear systems. We first obtain the Takagi-Sugeno (T-S) fuzzy model of the nonlinear plant by linear self-constructing neural fuzzy inference network (linear SONFIN). With training input and output data of the nonlinear system, linear SONFIN can dynamically increase the number of fuzzy rules, and also adjust the parameters of each rule to minimize the output error. Then, if each fuzzy subsystems is completely controllable and completely observable, we can apply the optimal fuzzy controller design scheme [24]-[26] to the proposed linear T-S fuzzy model. In the case of system model is unavailable, this approach can provide a way to stabilize and optimal control the physical system. Four examples are given to demonstrate the design procedure of this approach. 李祖添 2002 學位論文 ; thesis 55 zh-TW |
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碩士 === 國立交通大學 === 電機與控制工程系 === 90 === In this work, we propose an integrated approach to fuzzy modeling and optimal fuzzy control for unknown nonlinear systems. We first obtain the Takagi-Sugeno (T-S) fuzzy model of the nonlinear plant by linear self-constructing neural fuzzy inference network (linear SONFIN). With training input and output data of the nonlinear system, linear SONFIN can dynamically increase the number of fuzzy rules, and also adjust the parameters of each rule to minimize the output error. Then, if each fuzzy subsystems is completely controllable and completely observable, we can apply the optimal fuzzy controller design scheme [24]-[26] to the proposed linear T-S fuzzy model. In the case of system model is unavailable, this approach can provide a way to stabilize and optimal control the physical system. Four examples are given to demonstrate the design procedure of this approach.
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| author2 |
李祖添 |
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李祖添 林玟叡 |
| author |
林玟叡 |
| spellingShingle |
林玟叡 Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems |
| author_sort |
林玟叡 |
| title |
Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems |
| title_short |
Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems |
| title_full |
Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems |
| title_fullStr |
Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems |
| title_full_unstemmed |
Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems |
| title_sort |
neural network based optimal fuzzy controller design for nonlinear systems |
| publishDate |
2002 |
| url |
http://ndltd.ncl.edu.tw/handle/27089656634052171071 |
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