4.6 Article

Closed-Loop MRAS Speed Observer for Linear Induction Motor Drives

期刊

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 51, 期 3, 页码 2279-2290

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2014.2375377

关键词

Closed-loop model reference adaptive system (MRAS) (CL-MRAS); dynamic end effects; linear induction motor (LIM); sensorless control

资金

  1. RITmare, Ricerca ITaliana per il mare (Italian Research for the sea) [CUP: B91J11000740001]
  2. TESEO, Tecnologie ad alta Efficienza per la Sostenibilita Energetica ed ambientale Onboard (High efficiency technologies for onboard energy and environmental sustainability) [CUP: B61C12000850005]
  3. CNR per il Mezzogiorno (Advanced Technologies for Energy Efficiency and Sustainable Mobility) [CUP: B51J10001290001]

向作者/读者索取更多资源

This paper presents a closed-loop model reference adaptive system (CL-MRAS) speed observer developed for linear induction motor (LIM) drives. Starting from the structure of the CL-MRAS speed observer developed in the literature for rotating induction motors, a corresponding speed observer for LIMs has been developed here. It is based on the LIM dynamic model taking into consideration its dynamic end effects. In particular, the following aspects are original: 1) It employs the voltage and current models of the LIM considering its dynamic end effects, 2) it proposes a net thrust model including the braking force caused by the dynamic end effects; 3) it models the friction load of the LIM drive experimentally obtained with off-line tests. The proposed sensorless technique has been tested on a purposely developed experimental set-up. Results show that the CL-MRAS observer permits the drive to operate down to the speed of 0.01 m/s, i. e., about 0.15% of the rated speed, which is about 20 times lower than the minimum working speed achieved with other observers implemented on the same LIM drive, such as the total least squares (TLS) MRAS observer, the TLS EXIN full-order Luenberger observer, the extended Kalman filter, and, finally, the TLS EXIN Kalman filter.

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