A Hybrid Artificial Intelligence Algorithm to Determine the Speed and Position in Multi Operation Mode Sensorless Brushed D.C. Motor
Brushed direct current motor (BDCM) is an internal self-commutated electric motor which runs on direct current power source that are found in industrial and commercial applications. Traditional external BDCM sensors like hall-effect and optical encoders are too fragile, costly and complex for some applications. Thus, two classes of sensorless BDCM speed estimation techniques are used: back electro-motive force (BEMF) and ripple current sensing. Ripple current sensing method offers better accuracy by giving fixed discrete pulses for counting. However, it is overlaid with noises that are hard to filter. Many methods are devised for converting ripple current to pulses: comparator-filter, adaptive filter, predictive sensing and pattern recognition. These methods highlight only BDCM operations at near operating speed and neglect other common BDCM operating modes such as braking and coasting. This research will address all the issues which are crucial in real life applications where exact position and speed in all modes effect the accuracy of a system.
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Document Type: Research Article
Affiliations: Artificial Intelligence Research Unit (AiRU), Faculty of Engineering, Universiti Malaysia Sabah, 88400, Jalan UMS, Kota Kinabalu, Sabah, Malaysia
Publication date: November 1, 2017
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