Optimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motors

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dc.contributor.author Neri, Ferrante en
dc.contributor.author Salvatore, N. en
dc.contributor.author Caponio, A. en
dc.contributor.author Neri, Ferrante en
dc.contributor.author Stasi, S. en
dc.contributor.author Cascella, G. L. en
dc.date.accessioned 2012-08-08T15:40:16Z
dc.date.available 2012-08-08T15:40:16Z
dc.date.issued 2010-01
dc.identifier.citation Salvatore, 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. 385-394 en
dc.identifier.issn 0278-0046
dc.identifier.uri http://hdl.handle.net/2086/6730
dc.description.abstract This 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.iso en en
dc.subject AC motor drives en
dc.subject algorithms en
dc.subject covariance matrices en
dc.subject evolutionary algorithms (EAs) en
dc.subject induction-motor (IM) drives en
dc.subject Kalman filtering en
dc.subject optimization methods en
dc.subject parameter estimation en
dc.subject speed sensorless en
dc.subject state estimation en
dc.subject velocity control en
dc.title Optimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motors en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1109/TIE.2009.2033489
dc.researchgroup Centre for Computational Intelligence en


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