Controller decision making using neural networks
Most industrial processes contain nonlinearities, making them difficult to control. To overcome this issue many authors have developed complex nonlinear algorithms and models, most of them being process dependant. However, creating local models to approximate the plant by linear regions is a suita...
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sg-ntu-dr.10356-414202023-07-04T15:29:25Z Controller decision making using neural networks Andres Prado Espinoza Mao Kezhi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation Most industrial processes contain nonlinearities, making them difficult to control. To overcome this issue many authors have developed complex nonlinear algorithms and models, most of them being process dependant. However, creating local models to approximate the plant by linear regions is a suitable approach in most cases. This approach lets the engineer create local PID controllers and switch them according to the plant linear regions. Master of Science (Computer Control and Automation) 2010-07-02T08:13:27Z 2010-07-02T08:13:27Z 2008 2008 Thesis http://hdl.handle.net/10356/41420 en 59 p. application/pdf |
| institution |
Nanyang Technological University |
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NTU Library |
| continent |
Asia |
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Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| language |
English |
| topic |
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation Andres Prado Espinoza Controller decision making using neural networks |
| description |
Most industrial processes contain nonlinearities, making them difficult to
control. To overcome this issue many authors have developed complex nonlinear algorithms and models, most of them being process dependant. However, creating local models to approximate the plant by linear regions is
a suitable approach in most cases. This approach lets the engineer create local PID controllers and switch them according to the plant linear regions. |
| author2 |
Mao Kezhi |
| author_facet |
Mao Kezhi Andres Prado Espinoza |
| format |
Theses and Dissertations |
| author |
Andres Prado Espinoza |
| author_sort |
Andres Prado Espinoza |
| title |
Controller decision making using neural networks |
| title_short |
Controller decision making using neural networks |
| title_full |
Controller decision making using neural networks |
| title_fullStr |
Controller decision making using neural networks |
| title_full_unstemmed |
Controller decision making using neural networks |
| title_sort |
controller decision making using neural networks |
| publishDate |
2010 |
| url |
http://hdl.handle.net/10356/41420 |
| _version_ |
1772828614258065408 |