Abstract
Among all the control methods developed for Induction Motor (IM) drivers, the hysteresis controller based Direct Torque Control (DTC) method has an important place. This control method does not require other rotor and stator parameters except the stator resistance and does not require position or velocity sensors. However, there are some disadvantages of the DTC method, such as high torque, flux and current ripples. In this study, in order to reduce the high torque ripples occurring in an induction motor that is controlled by the hysteresis controller based conventional DTC method, a simple and effective Sugeno type Neuro-Fuzzy Torque Controller (NFTC) is proposed. This proposed controller is used instead of hysteresis controller. An experimental setup consisting of 1.1 kW induction motor, current and voltage measurement, DS1103 control card and two-level voltage source inverter was installed. To evaluate the performance of the proposed controller structure, various experimental studies were performed. Results obtained from the proposed NFTC based structure and conventional hysteresis controller based DTC structures are given comparatively. By the obtained experimental results, it was confirmed that the proposed NFTC-based controller structure considerably reduced flux and torque ripples in the motor.
Highlights
ASYNCHRONOUS MOTORS have been largely used for many years because of their simple structure, highstrength, reliability, robustness, low cost, and high efficiency
Torque, current, and flux responses of the experimental results given in Figures 12-18, it can be seen that the proposed Neuro-Fuzzy Torque Controller (NFTC) algorithm has decreased the torque ripple and the speed, flux, and current ripples
The Direct Torque Control (DTC) is preferred for highly dynamic applications, it shows high torque and current ripple
Summary
To be able to solve the above mentioned disadvantages of the conventional DTC, many research have been carried out by researchers on adaptive hysteresis band [5], improved switching table [7,8], space vector modulation approach and constant switching frequency [9,10,25], reduction of torque ripple [7,8,9,10,22,23], Intelligent control techniques [11,12], complex flux estimation methods [13], controller design [9,14,26,27], multilevel inverters [15], predictive control [16], genetic algorithm [19], parameter estimation with particle swarm optimization [20]. In the 5th part, the block diagram for the experimental design and experimental results are given
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