Self-tuning Based on Online Inertia Identification for Motor Drive

During the operation of the AC servo system, changes of system parameters and external load disturbances will affect the stability of the system. It is important to perform parameter self-tuning for obtaining high performance motor control. In this paper, a self-tuning method based on online inertia identification is proposed. The system motion equation is used as the reference model. The least squares recursive (RLS) algorithm and Kalman observer are used to obtain the system inertia value in real time. The identified results are used to adjust the speed loop controller in the motor drive. Simulations show that the proposed algorithm effectively improves the motor performance.

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