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dc.contributor.authorNeri, Ferranteen
dc.contributor.authorSalvatore, N.en
dc.contributor.authorCaponio, A.en
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorStasi, S.en
dc.contributor.authorCascella, G. L.en
dc.date.accessioned2012-08-08T15:40:16Z
dc.date.available2012-08-08T15:40:16Z
dc.date.issued2010-01
dc.identifier.citationSalvatore, N., Caponio, A., Neri, F. et al. (2010) Optimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motors. IEEE Transactions on Industrial Electronics, 57 (1), pp. 385-394en
dc.identifier.issn0278-0046
dc.identifier.urihttp://hdl.handle.net/2086/6730
dc.description.abstractThis paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a classical local search and three popular metaheuristics in terms of quality of the final solution for the problem considered in this paper. A novel simple stator-flux-oriented sliding-mode (SFO-SM) control scheme is online used in conjunction with the optimized DSKF-based algorithm to improve the robustness of the sensorless IM drive at low speed. The SFO-SM control scheme has closed loops of torque and stator-flux linkage without proportional plus- integral controllers so that a minimum number of gains has to be tuned.en
dc.language.isoenen
dc.subjectAC motor drivesen
dc.subjectalgorithmsen
dc.subjectcovariance matricesen
dc.subjectevolutionary algorithms (EAs)en
dc.subjectinduction-motor (IM) drivesen
dc.subjectKalman filteringen
dc.subjectoptimization methodsen
dc.subjectparameter estimationen
dc.subjectspeed sensorlessen
dc.subjectstate estimationen
dc.subjectvelocity controlen
dc.titleOptimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motorsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TIE.2009.2033489
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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