• ISSN: 2349-6002
  • UGC Approved Journal No 47859

A High Performance of Neural Network Model Controller for PMSM Drive

  • Unique Paper ID: 162324
  • Volume: 10
  • Issue: 9
  • PageNo: 218-223
  • Abstract:
  • 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.
email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 9
  • PageNo: 218-223

A High Performance of Neural Network Model Controller for PMSM Drive

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry