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A stochastic iterative learning control algorithm with application to an induction motor

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dc.contributor.author Saab, Samer S.
dc.date.accessioned 2019-07-30T06:48:56Z
dc.date.available 2019-07-30T06:48:56Z
dc.date.copyright 2004 en_US
dc.identifier.issn 0020-7179 en_US
dc.identifier.uri http://hdl.handle.net/10725/11170
dc.description.abstract A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. It is shown that, in the case where the number of inputs is not greater than the number of outputs, the input error covariance matrix converges to zero at a rate inversely proportional to the number of iterations in the presence of uncorrelated random state disturbance, reinitialization errors and measurement noise. The state error covariance matrix converges to zero at a rate inversely proportional to the number of iterations in the presence of measurement noise. In the case where the number of inputs is greater than the number of outputs, then the system output error converges to zero at a rate inversely proportional to the number of iterations in presence of measurement noise. Another suboptimal recursive algorithm is also proposed based on unknown system dynamics and unknown disturbance statistics. The convergence characteristics are shown to be similar to the ones of the optimal recursive algorithm. The proposed ILC algorithms are applied to two different models of an induction motor for angular speed tracking control. One model describes its dynamics in stator fixed (a, b) reference frame without current loops and the other model is also in stator fixed reference(a, b) reference frame but with high-gain current loops. The simulation results show good tracking performance in the presence of noise with erroneous model parameters and noise statistics. An open-loop control is also proposed to improve the tracking rate of the proposed ILC algorithms. en_US
dc.language.iso en en_US
dc.title A stochastic iterative learning control algorithm with application to an induction motor en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SOE en_US
dc.author.idnumber 199690250 en_US
dc.author.department Electrical And Computer Engineering en_US
dc.description.embargo N/A en_US
dc.relation.journal International Journal of Control en_US
dc.journal.volume 77 en_US
dc.journal.issue 2 en_US
dc.article.pages 144-163 en_US
dc.identifier.doi https://doi.org/10.1080/00207170310001646282 en_US
dc.identifier.ctation Saab, S. S. (2004). A stochastic iterative learning control algorithm with application to an induction motor. International Journal of Control, 77(2), 144-163. en_US
dc.author.email ssaab@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://www.tandfonline.com/doi/full/10.1080/00207170310001646282 en_US
dc.orcid.id https://orcid.org/0000-0003-0124-8457 en_US
dc.author.affiliation Lebanese American University en_US


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