文章基本信息

标题：Safe Learning for Distributed Systems with Bounded Uncertainties * * This work was supported by the Swiss National Science Foundation under grant no. PP00P2_157601 / 1 and by the Swiss National Centre of Competence in Research NCCR Digital Fabrication.
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作者：Rie B. Larsen ; Andrea Carron ; Melanie N. Zeilinger 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：2536-2542
DOI：10.1016/j.ifacol.2017.08.069
语种：English
出版社：Elsevier
摘要：AbstractLearning in interacting dynamical systems can lead to instabilities and violations of critical safety constraints, which is limiting its application to constrained system networks. This paper introduces two safety frameworks that can be applied together with any learning method for ensuring constraint satisfaction in a network of uncertain systems, which are coupled in the dynamics and in the state constraints. The proposed techniques make use of a safe set to modify control inputs that may compromise system safety, while accepting safe inputs from the learning procedure. Two different safe sets for distributed systems are proposed by extending recent results for structured invariant sets. The sets differ in their dynamical allocation to local sets and provide different trade-offs between required communication and achieved set size. The proposed algorithms are proven to keep the system in the safe set at all times and their effectiveness and behavior is illustrated in a numerical example.
关键词：Keywordsdistributed controlestimationcontrol of constrained systemssafe learning