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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Informatics and Communication Technology (IJ-ICT)
Anumala, Kalpana
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Axial flux machine performance enhancement using recurrent neural network controller Anumala, Kalpana; Veligatla, Ramesh Babu
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp740-750

Abstract

Traditional control methods often face limitations in optimizing the performance of these motors, especially in complex industrial and automotive applications where precision, stability, and energy efficiency are paramount. By exploring advanced control strategies such as multi-level inverters and neural network controllers, this study aims to overcome these limitations and unlock the full potential of dual rotor axial flux induction motors. The integration of multi-level inverters enables finer control of motor operation and enhances power quality, while neural network controllers offer adaptive and intelligent control capabilities, enabling the system to learn and optimize performance in real-time. The study investigates novel approaches to enhance the performance and efficiency of electric motor control systems. The study aims to address the challenges associated with traditional control methods and optimize the operation of dual rotor axial flux induction motors. The research evaluates various performance metrics associated with the speed control system, including error histograms, training performance, regression accuracy, rotor speed dynamics, rotor torque characteristics, time series analysis, and training state assessment. The study achieves significant milestones in optimizing system performance, as evidenced by key findings such as a low mean squared error (MSE) of 0.00011396 achieved during training, strong correlation in regression analysis with an R-value of 0.99718, and effective training dynamics indicated by a gradient value of 0.0091742 and a learning rate (Mu) of 0.0001. These results underscore the effectiveness and reliability of the proposed control strategies in improving motor performance, efficiency, and reliability while reducing energy consumption and operational costs. The proposed method is implemented using MATLAB.
Advanced control techniques for performance improvement of axial flux machines Anumala, Kalpana; Veligatla, Ramesh Babu
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1095-1107

Abstract

The topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications.
Co-Authors Veligatla, Ramesh Babu