{
  "$type": "site.standard.document",
  "description": "A method for determining MTPA, flux-weakening, and MTPV operating points over the full speed range of an IPM motor for the most efficient torque control of the motor using a neural network is provided. The neural network is trained using a cloud-based neural network training algorithm. A special…",
  "path": "/patents/1336703",
  "publishedAt": "2023-02-02T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W20/30",
    "The Board of Trustees of the University of Alabama"
  ],
  "textContent": "A method for determining MTPA, flux-weakening, and MTPV operating points over the full speed range of an IPM motor for the most efficient torque control of the motor using a neural network is provided. The neural network is trained using a cloud-based neural network training algorithm. A special technique is developed to generate neural network training data, that is particularly suitable and favorable, to develop a high-performance neural network-based IPM torque control system, and the impact of variable motor parameters is embedded into the neural network system development and training. The provided method can achieve a fast and accurate current reference generation with a simple neural network structure, for optimal torque control of an IPM motor. The method can handle the MTPA, MTPV, and flux-weakening operation considering physical motor constraints.",
  "title": "NEURAL-NETWORK BASED MTPA, FLUX-WEAKENING AND MTPV FOR IPM MOTOR CONTROL AND DRIVES"
}