A study of sensorless vector control of IM using neural network luenberger observer
| Autor: | Bousmaha Bouchiba, Tahar Belbekri, Ismail Khalil Bousserhane, Houcine Becheri |
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| Rok vydání: | 2020 |
| Předmět: |
Electronic speed control
Artificial intelligence Vector control Artificial neural network Computer science Stability (learning theory) Energy Engineering and Power Technology Neural network Luenberger Robustness (computer science) Control theory Asynchronous motor State observer Electrical and Electronic Engineering Reliability (statistics) Induction motor |
| Popis: | After the development of electronic components, the elimination of the sensors has become a necessary subject to get good results in the field of speed control, because of the price of the sensors, the strenuous choice of its position and the disturbance of measurement which affects the robustness of control. The luenberger observer showed to be one of the most excellent methods suggested by the researchers; this is due to the best performance, it offers in terms of stability, reliability and less counting effort. In this article, a study of luenberger observer based on neural network-based was discussed. This artificial intelligence method makes it possible to decrease the error of estimated speed for IRFOC control of the induction motor. Simulation results are obtained to show the robustness and stability of the system. |
| Databáze: | OpenAIRE |
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