Abstract
This work introduces an artificial neural network based speed control system for AC induction motors. In motor control, Radial Basis Function (RBF) network and Volt-Hertz (V/f) method have been adopted as neural controller and scalar driving tool respectively. V/Hz method has been preferred for the sake of simplicity and well-known reliability. The proposed control scheme consists of two main parts; a RBF network and a reference model unit. RBF based main controller supplies appropriate control signals to the driving unit to compensate nonlinearity of the induction motor and track the reference model output. Reference model, is very stable linear filter which is supplying set values to be imitated by induction motor. The success of the proposed control scheme has been demonstrated by experimental results; induction motor has been able to track the prescribed speed trajectory with rather small errors and good stability under properly loading conditions.
This work has been supported by KSU Research Fund with project number 2005/2-12.
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Özçalık, H.R., Yıldız, C., Danacı, M., Koca, Z. (2007). RBF Based Induction Motor Control with a Good Nonlinearity Compensation. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_106
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DOI: https://doi.org/10.1007/978-3-540-73007-1_106
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