Event-Triggered Control From Data

• 成果类型: Article
• 署名作者: De Persis, Claudio; Postoyan, Romain; Tesi, Pietro
• 署名单位: University of Groningen; University of Groningen; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; University of Florence
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3335002
• 发表日期: 2024
• 页码: 3780-3795
• 关键词: Noise measurement; sensors; data models; Closed loop systems; Linear systems; asymptotic stability; actuators; Data-driven control; event-triggered control; Learning systems; Linear matrix inequalities; networked control systems; Robust control

摘要

We present a data-based approach to design event-triggered state-feedback controllers for unknown continuous-time linear systems affected by disturbances. By an event, we mean state measurements transmission from the sensors to the controller over a digital network. By exploiting a sufficiently rich finite set of noisy state measurements and inputs collected off-line, we first design a data-driven state-feedback controller to ensure an input-to-state stability property for the closed-loop system ignoring the network. We then take into account sampling induced by the network and we present robust data-driven triggering strategies to (approximately) preserve this stability property. The approach is general in the sense that it allows deriving data-based versions of various popular triggering rules of the literature. In all cases, the designed transmission policies ensure the existence of a (global) strictly positive minimum interevent time thereby excluding Zeno phenomenon despite disturbances. These results can be viewed as a step towards plug-and-play control for networked control systems, i.e., mechanisms that automatically learn to control and to communicate over a network.