Extended Luenberger Observer Based on Dynamic Neural Network for Inertia Identification in PMSM Servo System
Natural Computation, 2009. ICNC '09. Fifth International Conference（2009）
摘要
A new scheme to estimate the moment of inertia in the motor drive system in very low speed is proposed. The simple speed estimation scheme, which is used in most servo systems for low-speed operation, is sensitivity to variations in machine parameters especially the moment of inertia. To estimate the motor inertia value, an extended Luenberger observer (ELO) is applied. The observer gain matrix can be adjusted on-line based on dynamic neural network. The effectiveness of the proposed ELO is verified by simulation results.
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关键词
moment of inertia,new scheme,simple speed estimation scheme,low-speed operation,observer gain matrix,dynamic neural network,proposed elo,inertia estimation,servomotors,motor drive system,estimation scheme,low speed,servo system,permanent magnet motors,motor inertia value,inertia identification,synchronous motors,pmsm servo system,extended luenberger observer,neural nets,artificial neural networks,mathematical model,torque
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