Speed control of high performance permanent magnet motors

• Main Author: Hoque, M. Ashraful, 1962-
• Other Authors: Memorial University of Newfoundland. Faculty of Engineering and Applied Science
• Format: Thesis
• Language: English
• Published: 1996
• Subjects: Permanent magnet motors; Electronic controllers; Neural networks (Computer science); Newfoundland studies; University of Newfoundland
• Online Access: http://collections.mun.ca/cdm/ref/collection/theses3/id/50356

Thesis (Ph.D.)--Memorial University of Newfoundland, 1996. Engineering and Applied Science Bibliography: leaves 181-191 This thesis presents a novel technique of speed control for permanent magnet (I'M) motors. Robust and precise speed control is of critical importance in the high performance drive applications. Unavoidable system disturbances, such as parameter variations, effects of sudden load impact and other system noises are resolved by developing on-line self tuning artificial neural network control structures fur both PM dc and PM brushless synchronous motor drives. The newly devised artificial neural network controllers are capable of overcoming the limitations of model dependent conventional fixed gain and existing adaptive speed controllers. -- Utilizing the concepts of inverse motor dynamics and non-linear load characteristics, artificial neural network based controllers arc designed on the basis of feed-forward neural networks. The transient arid dynamic behaviors of the proposed drive systems are improved by incorporating a unique feature of adaptive- learning rate which aids the on-line robust speed control over a wide operating range. The stability of the proposed systems has been ensured by a combination of off-line and on-line: trainings of the artificial neural networks. -- As an integral part of this work, efforts have been directed at the real-time: implementation of the artificial neural network based PM motor drive systems using a digital signal processor (DSP) controller board-OS 1102. A new circuit topology has been developed in order to lessen the computation burden of the DSP controller board for the implementation of the PM brushless synchronous motor drive system. A series of tests has been carried out with both PM dc and PM synchronous motors in order to evaluate the performances of the artificial neural network based drive systems. The laboratory test results validate the feasibility of the artificial neural network as an adaptive controller in the high performance drives.