标题：Contribution to the Artifical Neural Network Speed Estimator in a Degraded Mode for Sensor-Less Fuzzy Direct Control of Torque Application Using Dual Stars Induction Machine
其他标题：Contribution to the Artifical Neural Network Speed Estimator in a Degraded Mode for Sensor-Less Fuzzy Direct Control of Torque Application Using Dual Stars Induction Machine
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2015
卷号：5
期号：4
页码：729-741
DOI：10.11591/ijece.v5i4.pp729-741
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Recently one of the major topic of research is the involvement of the intelligence artificial in the control system. This paper deals with application of a new combination between two-control strategy known as fuzzy direct control of torque and then an adaptive Neuronal Speed estimator utilizing dual starts induction motor. The research discussed consist to replace the switching table used in the conventional direct control method and adaptive mechanism of the classic MRAS estimator with fuzzy controller and new neural network accordingly, both strategies can manage the degraded and normal modes. The neural networks used are the back-propagation, to reduce the training patterns and increase the execution speed of the training process. As results we achieved can be summarised as follows: 1) high degree of reliability of speed estimation even with using only one start voltages and currents and parameters; 2) Minimization of the torque and flux ripples; and 3) Minimization of the current total harmonic distortion.
其他摘要：Recently one of the major topic of research is the involvement of the intelligence artificial in the control system. This paper deals with application of a new combination between two-control strategy known as fuzzy direct control of torque and then an adaptive Neuronal Speed estimator utilizing dual starts induction motor. The research discussed consist to replace the switching table used in the conventional direct control method and adaptive mechanism of the classic MRAS estimator with fuzzy controller and new neural network accordingly, both strategies can manage the degraded and normal modes. The neural networks used are the back-propagation, to reduce the training patterns and increase the execution speed of the training process. As results we achieved can be summarised as follows: 1) high degree of reliability of speed estimation even with using only one start voltages and currents and parameters; 2) Minimization of the torque and flux ripples; and 3) Minimization of the current total harmonic distortion.
