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Development and analysis of a self-tuned neuro-fuzzy controller for induction motor drives

dc.contributor.advisorUddin, Mohammad
dc.contributor.authorWen, Hao
dc.date.accessioned2017-06-07T20:14:11Z
dc.date.available2017-06-07T20:14:11Z
dc.date.created2005
dc.date.issued2005
dc.identifier.urihttp://knowledgecommons.lakeheadu.ca/handle/2453/3291
dc.description.abstractInduction motors (IM) have been widely utilized in industry for variable speed drives due to some of their advantages, such as rugged construction, low cost and reliable service with easy maintenance, as compared to conventional dc motors. For variable speed drive applications, the controller plays an important role so that the motor can follow the reference trajectories without any significant deviation. Furthermore, a controller which can provide fast speed response and handle uncertainties and disturbances, is absolutely necessary for high performance drive systems. Traditionally, fixed gain proportional-integral (PI) and some adaptive controllers have been utilized in industry for a long time. However, there are some disadvantages of these controllers to handle uncertainties which are inherent to a nonlinear IM. As a result, recently researchers paid their attention to apply intelligent algorithms to control the IM for high performance variable speed drive applications. Intelligent algorithms such as fuzzy logic (FL), neural network (NN), neuro-fuzzy (NF), etc, have inherent advantages as compared to the conventional controllers. In this thesis, a novel neuro-fuzzy controller (NFC) has been developed for speed control o f EM. For the complete drive, the indirect field orientation control is utilized in order to decouple the torque and flux controls. Thus, the induction motor can be controlled like a dc motor and hence the high performance can be achieved without lacking the advantage o f ac over dc motors. The proposed neuro-fuzzy controller incorporates Sugeno model based fuzzy logic laws with a five-layer artificial neural network (ANN) scheme. The controller is designed for low computational burden, which will be suitable for real-time implementation. Furthermore, for the proposed NFC an improved self-tuning method is developed based on the IM theory and its high performance requirements. The main task o f the tuning method is to adjust the parameters o f the fuzzy logic controller (FLC) in order to minimize the square of the error between actual and reference output. In this thesis, a model reference adaptive flux (MRAF) observer is also developed to estimate the d-axis rotor flux linkage in both constant flux and flux weakening regions based on motor voltage, current and reference trajectories for flux linkage. Thus, it provides safe operation to control the motor at high speeds, especially, above the rated speed. The d-axis reference flux linkage of the indirect field oriented control is provided by flux weakening method. Furthermore, a proportional-integral (PI) based flux controller is used to provide the compensation for the reference flux model by comparing the flux reference and the observed flux from Gopinath model flux observer. A complete simulation model for indirect field oriented control of IM incorporating the proposed MRAF observer based NFC is developed in Matlab/Simulink. In order to prove the superiority of the proposed controller, the performance of the proposed controller is compared with a conventional PI as well as fuzzy logic controller (FLC) based IM drives. The performance of the proposed IM drive is investigated extensively at different operating conditions in simulation. The performance of the proposed MRAF observer based NFC controller is found robust and a potential candidate for high performance industrial drive applications.
dc.language.isoen_US
dc.subjectElectric motors, Induction Automatic control
dc.subjectFuzzy logic
dc.subjectInduction motors
dc.titleDevelopment and analysis of a self-tuned neuro-fuzzy controller for induction motor drives
dc.typeThesis
etd.degree.nameMaster of Science
etd.degree.levelMaster
etd.degree.disciplineEngineering
etd.degree.grantorLakehead University


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