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Event-Triggered Control From Data
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DOI：10.1109/TAC.2023.3335002.png)
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Abstract
En 中文
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.
Keywords:
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
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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