文章基本信息

标题：Iterative Learning Approaches for Discrete-Time State and Disturbance Observer Design of Uncertain Linear Parameter-Varying Systems
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作者：Andreas Rauh ; Julia Kersten ; Harald Aschemann 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：15
页码：241-246
DOI：10.1016/j.ifacol.2019.11.681
语种：English
出版社：Elsevier
摘要：Iterative learning approaches are well developed for the design of feedback controllers of dynamic systems which perform identical control tasks during multiple successive executions. Such tasks are characterized by identical reference trajectories in each of the iterations to be run. Exploiting this information, it becomes possible to reduce tracking errors by adapting the control signal not only on the basis of information of the state variables from the current iteration but also by accounting for the history of the tracking errors from previous trials. Although such control strategies are part of an active field of research, the corresponding dual task, namely, the design of state and disturbance observers for systems with successively repeated state trajectories has not yet received the same amount of attention. Therefore, a discrete-time iterative learning observer design, aiming at the combined state and disturbance estimation, is presented in this paper. The resulting approach is validated in numerical simulations for the simplified model of the charging/discharging dynamics of Lithium-Ion battery systems.
关键词：KeywordsObserver designiterative learning approacheslinear matrix inequalitiesLyapunov stabilitydisturbance estimation