A Direct Adaptive Vector Neural Control of a Three-Phase Induction Motor
The paper proposes a complete neural solution to the direct vector control of three phase induction motor including realtime trained neural controllers for velocity, flux and torque, which permitted the speed up reaction to the variable load. The basic equations and elements of the direct field orie...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | México |
| Institución: | Instituto Politécnico Nacional |
| Repositorio: | Redalyc-IPN |
| OAI Identifier: | oai:redalyc.org:61412466005 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=61412466005 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería Levenberg Induction Motor Neural Networks Backpropagation Marquardt Learning |
| Sumario: | The paper proposes a complete neural solution to the direct vector control of three phase induction motor including realtime trained neural controllers for velocity, flux and torque, which permitted the speed up reaction to the variable load. The basic equations and elements of the direct field oriented control scheme are given. The control scheme is realized by nine feedforward neural networks learned by real-time Backpropagation or off-line Levenberg-Marquardt algorithms with data taken by PI-control simulations. The graphical results of modelling show a better performance of the neural control system with respect to the PI controlled system realizing the same general control scheme. |
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