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Intelligent Adaptive and Sensorless Control of Permanent Magnet Synchronous Linear Motor
Paperback
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ISBN10: 9994982745
ISBN13: 9789994982745
Publisher: Eliva Pr
Published: Sep 16 2022
Pages: 74
Weight: 0.24
Height: 0.15 Width: 6.00 Depth: 9.00
Language: English
ISBN13: 9789994982745
Publisher: Eliva Pr
Published: Sep 16 2022
Pages: 74
Weight: 0.24
Height: 0.15 Width: 6.00 Depth: 9.00
Language: English
With the rapid development of military and aerospace industries, the performance requirements of numerical control equipment are constantly improving. Permanent magnet linear synchronous motor (PMLSM) has the advantages of high speed and high precision. Meanwhile, it overcomes the influence of various factors in the intermediate links such as the screw and gear in the rotating motor. Therefore, PMLSM has very important value in high-precision industrial servo applications that require high-precision linear servo drivers.
This book mainly introduces the intelligent vector control strategy and sensorless control strategy of PMLSM. Compared with the traditional control strategy, the intelligent vector control strategy of the motor can better improve the control performance of the motor. In modern motor control systems, many new intelligent vector control strategies have been proposed. This paper focuses on the sliding mode control strategy, model predictive control strategy, adaptive control strategy, active disturbance rejection control strategy and model free control strategy of PMLSM. Relying on the mathematical model of PMLSM, the formula derivation of the above intelligent control strategies is explained in detail, and the system block diagram is drawn to help readers better understand the basic working principle of these intelligent control strategies.
In the control system of the motor, in order to achieve better control performance, accurate speed information and mover position information of the PMLSM are required. The traditional collection of speed information and mover position information is realized by adding mechanical sensors, but the addition of mechanical sensors will lead to an increase in the cost of the motor. Meanwhile, the size and weight of the motor will be affected, and there will be some additional interference factors, which will have a certain impact on the control performance of the motor. In order to make the motor get rid of the dependence on mechanical sensors, the sensorless control strategy is introduced into the motor control system, and the position information and speed information of the motor are estimated through a certain control algorithm. At present, the common sensorless control strategies are high-frequency signal injection method and fundamental back EMF observation method. This paper introduces the design of the sliding mode observation method, the observation method based on the model reference adaptive system and the observation method based on the extended Kalman filter algorithm. The above sensorless control strategies are introduced in detail through the corresponding formula derivation and system block diagram.
In the control system of the motor, in order to achieve better control performance, accurate speed information and mover position information of the PMLSM are required. The traditional collection of speed information and mover position information is realized by adding mechanical sensors, but the addition of mechanical sensors will lead to an increase in the cost of the motor. Meanwhile, the size and weight of the motor will be affected, and there will be some additional interference factors, which will have a certain impact on the control performance of the motor. In order to make the motor get rid of the dependence on mechanical sensors, the sensorless control strategy is introduced into the motor control system, and the position information and speed information of the motor are estimated through a certain control algorithm. At present, the common sensorless control strategies are high-frequency signal injection method and fundamental back EMF observation method. This paper introduces the design of the sliding mode observation method, the observation method based on the model reference adaptive system and the observation method based on the extended Kalman filter algorithm. The above sensorless control strategies are introduced in detail through the corresponding formula derivation and system block diagram.
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