Support vector machine-based fuzzy self-learning control for induction machines

The Three Phase Induction Motor (TIM) is one of the most widely used motors due to its low price, robustness, low maintenance cost, and high efficiency. In this paper, a Support Vector Regression (SVR) based controller for TIM speed control using Indirect Vector Control (IVC) is presented. The IVC method is more frequently used because it enables better speed control of the TIM with higher dynamic performance. Artificial Neural Network (ANN) controllers have been widely used for TIM speed control for several reasons such as their ability to successfully train without prior knowledge of the mathematical model, their learning ability, and their fast implementation speed. The SVR-based controller overcomes the drawbacks of the ANN-based controller, i.e. its low accuracy, overfitting, and poor generalization ability. The speed response under the proposed controller is faster in terms of rising and settling time. The dynamic speed response of the proposed controller is also superior to that of the ANN-PI controller. The performance of the proposed controller was compared for TIM speed control with an ANN-PI controller via simulations in SIMULINK.

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Support vector machine-based fuzzy self-learning control for induction machines

Cited by 2 publications

References 4 publications

Neuro-Fuzzy based on support vector machine friction compensator in servo hydraulic system

Neuro-Fuzzy based on support vector machine friction compensator in servo hydraulic system

Robust Speed Control of a Three Phase Induction Motor Using Support Vector Regression

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