A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems
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Publication date
2022-03Keyword
Non-Gaussian stochastic systemsProbability density function control
Kernel density estimation
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Show full item recordAbstract
This note presents a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing stVersion
Accepted manuscriptCitation
Zhang Q and Wang H (2022) A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems. IEEE Transactions on Automatic Control. 67(3): 1506-1513.Link to Version of Record
https://doi.org/10.1109/TAC.2021.3064991Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1109/TAC.2021.3064991