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    Data-driven minimum entropy control for stochastic nonlinear systems using the cumulant-generating function

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    Zhang_IEEE_Transactions_on_Automatic_Control.pdf (990.8Kb)
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    Publication date
    2022
    Author
    Zhang, Qichun
    Zhang, J.
    Wang, H.
    Keyword
    Entropy
    Stochastic processes
    Optimization
    Probability density function
    Stochastic systems
    Random variables
    Nonlinear systems
    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.
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
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    Abstract
    This 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.
    URI
    http://hdl.handle.net/10454/19181
    Version
    Accepted manuscript
    Citation
    Zhang Q, Zhang J and Wang H (2022) Data-driven minimum entropy control for stochastic nonlinear systems using the cumulant-generating function. IEEE Transactions on Automatic Control. Accepted for Publication.
    Link to publisher’s version
    https://ieeexplore.ieee.org/document/9896147
    Type
    Article
    Collections
    Engineering and Informatics Publications

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