Neural Control of the Induction Motor Drive: Robust Against Rotor and Stator Resistances Variations and Suitable for Very Low and High Speeds

In this paper, induction motor speed control drive is designed with application two multilayer feed-forward neural networks. That those are used one for generate PWM pulse and other for estimation of required torque and flux information. For trained of the PWM wave generate neural network is used from compound information two voltage and current classic model. Also, against general classic models for generate of the switching pulses is used as compound from reference voltage and current two motor phases. With these ideas are eliminated problems of the voltage and current classic models (flux saturation in current model for high speeds and voltage drop in voltage model for low speeds). As voltage profile is improved in this paper. The required feedback signals estimation (including: rotor flux, torque, etc.) is estimated by multilayer feed-forward neural network. That for robustness of the above estimator against rotor and stator resistances variations in time work of motor is used from compound trained data of the voltage and current classic models, because the voltage and current of the general classic models to sequence are independent of rotor and stator resistances. The simulation results by MATLAB-Simulink verify the proposed drive in improvement of the speed profile in transient and steady-state operating modes. Also, it verify clearly robust of the proposed drive against rotor and stator resistances variations in time work.