Volume 04,Issue 04

An Intelligent Method Based on Model Predictive Torque Control and Optimized ANFIS for Induction Motor Speed Control

Authors

Reza Zamani Shourabi, Mohammad Reza Moradian


Abstract
A large number of motors are being used for general purposes in our surroundings from house-hold equipment to machine tools in industrial facilities. The electric motor is now a necessary and indispensable source of power in many industries. Three-phase induction motors are widely used as industrial drives because they are self-starting, reliable and economical. In this paper, an intelligent method based on Model Predictive Torque Control (MPTC) and optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for a three-phase induction motor speed control. The proposed method includes the inverter model in control design and doesn't use any modulation block. The optimal selection of inverter switching states minimizes the error between references and the predicted values of control variables by the optimization of a cost function. Consequently, it reduces ripples and solves DTC drawbacks. Furthermore, this paper proposes an improvement in the external speed loop for MPTC scheme. An ANFIS controller replaces the traditional Proportional-Integrator (PI) controller to ensure more accurate speed tracking and increase the robustness against disturbance and uncertainties. In the proposed method, an adaptive and hybrid artificial bee colony (aABC) algorithm is used to train the ANFIS. aABC algorithm is one of the most powerful and accurate optimization algorithms which is introduced recently. The proposed method is compared with conventional DTC and other methods by computer simulation through the MATLAB/SIMULINK software. The obtained results show the superior performance of the proposed method in comparison with other methods.

Keyword: ANFIS, aABC, MPTC, DTC, Induction motor.

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