Application of Artificial Neural Network Control Techniques for Torque Ripple Mitigation in Bearingless Synchronous Motors
Keywords:
Artificial neural network control, Bearingless synchronous motor, Electromagnetic performance optimization, Intelligent motor control, Torque ripple mitigationAbstract
This paper investigates the application of Artificial Neural Network (ANN)-based control techniques for mitigating torque ripple in bearingless synchronous motors, a critical challenge in high-precision electric drive systems. The increasing deployment of electric motors in industrial automation, robotics, renewable energy, and aerospace applications has intensified the demand for advanced control strategies that ensure smooth torque production, reduced vibration, and reliable long-term operation. Torque ripple in synchronous motor drives leads to periodic torque pulsations that cause mechanical stress, acoustic noise, performance degradation, and reduced service life. The main objective of this study is to design and evaluate an ANN-based control framework capable of effectively minimizing torque ripple under varying operating conditions. The proposed method exploits the nonlinear mapping and adaptive learning capabilities of neural networks to dynamically adjust control parameters in response to system variations. A detailed motor drive model was developed and implemented in the MATLAB/Simulink environment using operational data obtained from industrial installations to enhance practical relevance. Two industrial motor samples from Nigeria were used for validation: an 11.0 kW, 400 V motor from the BUA industrial facility and an 11.0 kW, 690 V motor from the Indorama plant, both with distinct speed and efficiency ratings. Simulation results show that the ANN-based controller achieved approximately 35.4% reduction in torque ripple compared with conventional control methods, leading to smoother torque profiles, reduced mechanical loading, lower noise emission, and improved control accuracy. The findings confirm the effectiveness and robustness of ANN-based control for torque ripple mitigation in bearingless synchronous motor applications.
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