所属单位：曼彻斯特大学
教研室：电气与电子工程学院，控制系统中心
发表刊物：Systems Science and Control Engineering
刊物所在地：Taylor & Francis
项目来源：中国国家自然科学基金，项目号：61573022、61290323、61333007。
关键字：Reduced-order closed-form covariance model, parametric covariance assignment, stochastic systems.
摘要：This paper presents a novel closed-form covariance model using covariance matrix decomposition for both continue-time and discrete-time stochastic systems which are subjected to Gaussian noises. Different from the existing covariance models, it has been shown that the order of the presented model can be reduced to the order of original systems and the parameters of the model can be obtained by Kronecker product and Hadamard product which imply an uniform expression. Furthermore, the associated controller design can be simplified due to the use of the reduced-order structure of the model.
备注：EI, Accession #: 20161502234354。 Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tssc20
合写作者：Hong Wang,王卓,Qichun Zhang*
论文类型：基础研究
文献类型：期刊
是否译文：否
发表时间：2016-05-05