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| dc.contributor.author | Belay, Agmuasie | |
| dc.date.accessioned | 2020-06-08T07:09:16Z | |
| dc.date.available | 2020-06-08T07:09:16Z | |
| dc.date.issued | 2019-10 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/10958 | |
| dc.description.abstract | Induction motors are used in wide range of industrial applications as variable speed drives and motion control. Their nonlinearity, time variant dynamics and inaccessible states and outputs impose too much difficulty to control them. Direct torque control of induction motor was one prospective control that has been applied to induction motor. Itssimplicityandnovelcontrolcharacteristicswasdegradedbynotabletorqueandflux ripples. Space vector modulation with direct torque control partially solved these limitations. But issues regarding robustness to parameter variation and load disturbance has not been resolved. Consequently, the integer order sliding mode torque control has been applied to induction motor control to mitigate these drawbacks. The ripple band andchatteringphenomenaofslidingmodecontrolcanbemodifiediftheslidingsurface is fractional order. In this thesis, space vector modulation based sliding mode control of induction motor is proposed. A fractional order proportional integral sliding surface has also been designed. Furthermore a method of estimating the stator flux, torque and flux angle using artificialneuralnetworksisincluded. TheLyapunovstabilityanalysishasbeenusedanalyze stability of the proposed controller. Simulation results confirm the effectiveness and validity of the proposed control method . The sliding mode torque control along with the estimator found to be insensitive to parameter variation and load disturbance. In addition, the chattering has been reduced by employing super twisting algorithm for speed control. With fractional sliding mode control, the torque ripple band was modified by±6Nm. Similarly, the flux ripple band has been modified by±0.04Wb. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Control systems | en_US |
| dc.title | Neural Network Based Flux Estimation and Fractional Order Sliding Mode Torque Control of Induction Motor | en_US |
| dc.type | Thesis | en_US |
