Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2951
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dc.contributor.authorMohanty, Kanungo Barada-
dc.contributor.authorMishra, Rabi Narayan-
dc.date.accessioned2018-03-14T11:20:00Z-
dc.date.available2018-03-14T11:20:00Z-
dc.date.issued2018-03-
dc.identifier.citation15th International Workshop on Advanced Motion Control (AMC-2018), Tokyo, Japan, 9 - 11 March, 2018.en_US
dc.identifier.urihttp://hdl.handle.net/2080/2951-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThis paper presents a development of a simplified neuro-fuzzy control (NFC) based on genetic algorithm (GA) for optimal performance of induction motor (IM) drive using feedback linearization (FBL) approach. An intuitive linearization technique based IM is modeled and simulated in the stationary dq reference frame. The proposed simplified NFC with GA (SNFC-GA) incorporated with FBL reduces the torque ripple and improves the speed response of the IM drive. This novel technique also has the benefit of reduced computational burden by improving computational efficiency over conventional NFC and thus, suitable for real-time industrial applications. Moreover, the optimal parameters of the modified NFC are searched by GA in order to ensure the global convergence of tracking error. The effectiveness of the proposed method using linearized IM drive is investigated in simulation as well as in experiment, and it is evident that the system provides optimal dynamic performance and is robust in terms of parameter variations and external load.en_US
dc.subjectFeedback linearizationen_US
dc.subjectInduction motoren_US
dc.subjectSimplified NFCen_US
dc.subjectGenetic algorithmen_US
dc.titleRobust Modified Structured NFC Integrating with GA for Linearized Induction Motor Driveen_US
dc.typeArticleen_US
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