Butt, Casey Benjamin (2007) Intelligent speed control of interior permanent magnet motor drives. Doctoral (PhD) thesis, Memorial University of Newfoundland.
- Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
The advantages of the interior permanent magnet synchronous motor (IPMSM) for modem high performance drive (HPD) applications are well established. The IPMSM has become increasingly popular in these applications due to its high torque to current ratio, high power to weight ratio, high efficiency, high power factor, low noise and robust operation. In HPDs, it is required that automated control systems be implemented that can respond quickly and accurately to sudden and unknown disturbances, recovering the correct working speed of the motor promptly. Conventional proportional-integral (PI) and proportional-integral-derivative (PID) speed controllers have been widely utilized to meet these control challenges in IPMSM drives. However, difficulties in obtaining the exact d-q axis reactance parameters of the IPMSM make design approaches for these controllers cumbersome. Moreover, since the operation of the IPMSM is strongly affected by the rotor magnetic saliency, saturation and armature reaction effects, conventional fixed-gain PI and PID controllers are very sensitive to parameter variations, changes of command speed and load disturbances. Therefore, an intelligent speed controller is required, which overcomes these problems, for the IPMSM drive to be used in HPD systems. -- This thesis presents intelligent speed controllers for the IPMSM based on multiple and single artificial neuron networks. Traditional artificial neural network-based motor controllers require extensive offline training, which is both time consuming and requires complex knowledge of motor behaviour for the specific drive system. In addition, drive behaviour is unpredictable when operating parameters outside the training set are encountered. The proposed drive systems overcome these limitations by utilizing a novel method of determining a reference command torque that is used to train the artificial neural network-based controller. Thus, no offline training of the networks is required, they are robust under varying operating conditions and they are adaptable to various drive systems. -- Control of the IPMSM at rated speed and below is achieved by use of the maximum torque per ampere (MTPA) mode of operation utilizing a series approximated approach. Control above base speed is achieved based on an approximation of the flux-weakening (FW) mode of operation. -- Comparisons are made, in simulation and experimentally, between the proposed drives and a conventional PID-based control technique utilizing the id = 0 approximation. -- The complete vector control scheme is implemented in real-time using a digital signal processor (DSP) controller board and a laboratory 1 hp interior permanent magnet synchronous motor.
|Item Type:||Thesis (Doctoral (PhD))|
|Additional Information:||Includes bibliographical references (leaves 210-223).|
|Department(s):||Engineering and Applied Science, Faculty of|
|Library of Congress Subject Heading:||Electric driving--Automatic control; Electronic controllers; Neural networks (Computer science); Permanent magnet motors--Automatic control.|
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