This model demonstrates possibility to use a feedforward neural network (static neural network) to estimate (more precisely to approximate) mechanical speed of the induction motor. The main challenge in such a task is to construct an effective approximation base. Here the 6 heuristic signals described in http://dx.doi.org/10.1109/EURCON.2007.4400678 are used. It should be noted that any estimation method that exploits only fundamental excitation (fundamental model) fails at zero stator flux frequency. The induction motor becomes an unobservable system at zero stator frequency. The proposed solution also has this limitation. If control at zero frequency is required, other techniques have to be implemented. For more hints see e.g. http://dx.doi.org/10.1109/TIE.2005.862324 . For some details on the analytical controller tuning in this particular drive, please see https://www.mathworks.com/matlabcentral/fileexchange/52706-dsfoc-drive-and-kessler-s-criteria .
Bartlomiej Ufnalski (2022). Speed-sensorless induction motor drive (https://www.mathworks.com/matlabcentral/fileexchange/48012-speed-sensorless-induction-motor-drive), MATLAB Central File Exchange. Retrieved .
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