文章基本信息

标题：Effective recursive parallelotopic bounding for robust output-feedback control
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作者：Udit Sharma ; Sakthi Thangavel ; Anwesh Reddy Gottu Mukkula 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：1032-1037
DOI：10.1016/j.ifacol.2018.09.058
语种：English
出版社：Elsevier
摘要：AbstractIn this paper an approach is studied to guaranteed (set-membership) state estimation for robust output-feedback model predictive control (MPC) with hard input and state constraints. Uncertainties are assumed to arise in a dynamic system from unknown initial conditions of state variables and due to unknown-but-bounded measurement noise. The uncertainty in the state variables is represented as a parallelotopic set. The employed state-estimation algorithm recursively outbounds the feasible set that is given by an intersection of model predictions with obtained measurement information. Along with the well-known minimum-volume criterion for parallelotopic outbounding, three alternative criteria are proposed and studied. The aim is to identify the best outbounding approach for improving performance of the robust MPC.
关键词：Keywordsstate estimationestimation algorithmsoutput feedbackrobust control