Cogging effect minimization in PMSM position servo system using dual high-order periodic adaptive learning compensation

Cogging effect which can be treated as a type of position-dependent periodic disturbance, is a serious disadvantage of the permanent magnetic synchronous motor (PMSM). In this paper, based on a simulation system model of PMSM position servo control, the cogging force, viscous friction, and applied l...

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Bibliographic Details
Published in:ISA transactions 2010-10, Vol.49 (4), p.479-488
Main Authors: Luo, Ying, Chen, YangQuan, Pi, YouGuo
Format: Article
Language:English
Subjects:
A.c. Machines
Adaptive control
Adaptive systems
Algorithms
Applied sciences
Artificial Intelligence
Asymptotic properties
Cogging
Cogging force
Compensation
Computer Simulation
Control systems
D.c. Machines
Dual high-order periodic adaptive learning control
Electrical engineering. Electrical power engineering
Electrical machines
Electronics - statistics & numerical data
Equipment Design - statistics & numerical data
Exact sciences and technology
Learning
Magnetics
Mathematical models
Neural Networks (Computer)
Nonlinear Dynamics
Permanent magnet synchronous motor (PMSM)
Reference Standards
State-dependent disturbance
Synchronous motors
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Summary:Cogging effect which can be treated as a type of position-dependent periodic disturbance, is a serious disadvantage of the permanent magnetic synchronous motor (PMSM). In this paper, based on a simulation system model of PMSM position servo control, the cogging force, viscous friction, and applied load in the real PMSM control system are considered and presented. A dual high-order periodic adaptive learning compensation (DHO-PALC) method is proposed to minimize the cogging effect on the PMSM position and velocity servo system. In this DHO-PALC scheme, more than one previous periods stored information of both the composite tracking error and the estimate of the cogging force is used for the control law updating. Asymptotical stability proof with the proposed DHO-PALC scheme is presented. Simulation is implemented on the PMSM servo system model to illustrate the proposed method. When the constant speed reference is applied, the DHO-PALC can achieve a faster learning convergence speed than the first-order periodic adaptive learning compensation (FO-PALC). Moreover, when the designed reference signal changes periodically, the proposed DHO-PALC can obtain not only faster convergence speed, but also much smaller final error bound than the FO-PALC.
ISSN:0019-0578
1879-2022
DOI:10.1016/j.isatra.2010.05.003