Development of Precise Encoder Edge-Based State Estimation for Motors

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We developed and implemented an algorithm named Precise Encoder Edge-based State Estimation for Motors (PEESEM). Instead of conventional periodic encoder sensing to obtain the quantized motor position, we detect the encoder edge's time and value precisely. Then, we use the edge-time Kalman filter (ETKF) containing predictions at edge time and periodic sampling time, and update at edge time. Only the first edge in each sampling interval is utilized to reduce the computation time at high motor speed. The proposed algorithm guarantees far more accurate state estimation with low encoder resolution and uncertainty on motor parameters. Performance of the proposed algorithm is validated through simulations and implementation on a two-wheeled mobile robot (TMR)
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-06
Language
English
Article Type
Article
Keywords

VELOCITY ESTIMATION; KALMAN-FILTER; SPEED ESTIMATION; SENSORLESS CONTROL; ACCELERATION; GENERATORS; OBSERVER

Citation

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, v.63, no.6, pp.3648 - 3655

ISSN
0278-0046
DOI
10.1109/TIE.2016.2539249
URI
http://hdl.handle.net/10203/209720
Appears in Collection
EE-Journal Papers(저널논문)
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