A High Performance of Neural Network Model Controller for PMSM Drive
CH. Siva Kumar, M. V. Ramana Rao
Permanent Magnet Synchronous Motor, Artificial Neural Network, Electrical drives.
This study introduces a novel neural network-driven controller designed to optimize the performance of Permanent Magnet Synchronous Motor (PMSM) drives. The controller harnesses the adaptive nature of neural networks to elevate the dynamic response and efficiency of PMSM drives amidst changing operational conditions. By amalgamating sophisticated control methodologies and neural network architectures, the controller achieves remarkable outcomes in speed regulation, torque precision, and resilience to parameter fluctuations. The neural network model undergoes training using advanced algorithms like backpropagation and reinforcement learning to dynamically grasp the nonlinear dynamics and disturbances inherent in PMSM drive systems. Through real-time feedback and online fine-tuning, the neural network controller adeptly counters the impacts of parameter uncertainties, load disruptions, and nonlinearities characteristic of PMSM drives. Empirical findings corroborate the efficacy and superiority of the proposed neural network-based controller over conventional control approaches. Moreover, the controller's scalability and adaptability render it suitable for diverse PMSM drive applications, spanning industrial automation to electric vehicles.
Article Details
Unique Paper ID: 162324

Publication Volume & Issue: Volume 10, Issue 9

Page(s): 218 - 223
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