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dc.contributor.authorZhang, Qichun
dc.contributor.authorZhang, J.
dc.contributor.authorWang, H.
dc.date.accessioned2022-09-27T09:17:04Z
dc.date.accessioned2022-10-21T11:26:34Z
dc.date.available2022-09-27T09:17:04Z
dc.date.available2022-10-21T11:26:34Z
dc.date.issued2023-08
dc.identifier.citationZhang Q, Zhang J and Wang H (2023) Data-driven minimum entropy control for stochastic nonlinear systems using the cumulant-generating function. IEEE Transactions on Automatic Control. 68(8): 4912-4918.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19181
dc.descriptionYesen_US
dc.description.abstractThis paper presents a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this paper, a data-driven minimum entropy design is presented without pre-knowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.en_US
dc.language.isoenen_US
dc.publisherIEEE
dc.rights© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectEntropyen_US
dc.subjectStochastic processesen_US
dc.subjectOptimizationen_US
dc.subjectProbability density functionen_US
dc.subjectStochastic systemsen_US
dc.subjectRandom variablesen_US
dc.subjectNonlinear systemsen_US
dc.titleData-driven minimum entropy control for stochastic nonlinear systems using the cumulant-generating functionen_US
dc.status.refereedYesen_US
dc.date.Accepted2022-09-14
dc.date.application2022-09-20
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.rights.licenseUnspecifieden_US
dc.date.updated2022-09-27T09:17:07Z
refterms.dateFOA2022-10-21T11:27:15Z
dc.relation.urlhttps://ieeexplore.ieee.org/document/9896147
dc.openaccess.statusopenAccessen_US


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