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Identification of Moment of Inertia for PMSM Using Improved Model-reference Adaptive System

Jinhua She, Lulu Wu, Chuan-Ke Zhang*, Zhen-Tao Liu, and Yonghua Xiong
International Journal of Control, Automation, and Systems, vol. 20, no. 1, pp.13-23, 2022

Abstract : The moment of inertia (MOI) is one of the most important parameters of a permanent magnet synchronous motor. High-precision identification of the MOI is essential to ensure system performance. This paper explains an MOI identification method based on the frame of an improved model-reference adaptive system (IMRAS). It improved a model-reference adaptive system by incorporating a curvature model and a dynamic gain in the system. First, a curvature model is used to estimate a load torque to construct an accurate reference model. This strategy reduces the identification error caused by ignoring the load torque. Note that identification accuracy and convergence speed are closely related to a gain factor in the system. Then, the relationship between the gain factor and the convergence time of the identification error is modeled as a power function. Finally, the IMRAS uses the absolute value of the relative MOI error and the convergence time for a given gain factor as switching conditions to balance the convergence speed and identification accuracy. A comparison with a conventional fixed-gain MRAS shows the effectiveness and superiority of the developed method.

Keyword : Curvature model, dynamic gain, identification of the moment of inertia (MOI), model-reference adaptive system (MRAS), permanent magnet synchronous motor (PMSM).

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