High Precision Speed Control of Salient Pole Permanent Magnet Synchronous Motor Based on Mtpa

Usama, Muhammad; Kim, J.

doi:10.1049/icp.2021.1203

IET Conf. Proc.

2021

DOI: 10.1049/icp.2021.1203

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High Precision Speed Control of Salient Pole Permanent Magnet Synchronous Motor Based on Mtpa

Muhammad Usama¹,

J. Kim²

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2022

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Inductionless control of high speed permanent magnet synchronous motor in full speed range based on neural network

Zheng¹

2022

JCM

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In order to solve the problems of high control error, large maximum output torque and long control time in the traditional full speed range sensorless control method of high-speed permanent magnet synchronous motor, a full speed range sensorless control method of high-speed permanent magnet synchronous motor based on neural network is proposed in this paper. Analyze the range of stator current trajectory in rotor coordinate system, so as to build the discrete model of permanent magnet synchronous motor, and build the whole process control model of high-speed permanent magnet synchronous motor by using neural network algorithm. Through the design of observer, overall control structure and speed regulation range of non inductance control, the goal of non inductance control in the full speed range of high-speed permanent magnet synchronous motor is achieved. The experimental results show that the non inductive control error in the full speed range of the high-speed permanent magnet synchronous motor is 0.2, the maximum output torque is 420 N⋅m, and reaches 2000 r/min at 0.6 s. The practical application effect is good.

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Inductionless control of high speed permanent magnet synchronous motor in full speed range based on neural network

Zheng¹

2022

JCM

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High Precision Speed Control of Salient Pole Permanent Magnet Synchronous Motor Based on Mtpa

Cited by 1 publication

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Inductionless control of high speed permanent magnet synchronous motor in full speed range based on neural network

Inductionless control of high speed permanent magnet synchronous motor in full speed range based on neural network

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