Data‐driven control of dynamic event‐triggered systems with delays

Abstract

This article studies data‐driven control of unknown sampled‐data linear systems with communication delays under an event‐triggering transmission mechanism. While event‐triggered control has received much attention in the existing literature, its design and implementation typically require detailed model knowledge. Due to the difficulties in finding accurate models and the abundance of data in many practical applications, we propose a novel data‐driven event‐triggered control scheme for unknown, delayed systems with theoretical guarantees. To this end, as a first step, a model‐based stability condition for the resulting event‐triggered time‐delay system is established using a novel looped‐functional approach. Combining this model‐based condition with the data‐driven representation of linear systems, a data‐based stability condition is derived. Further, methods for co‐designing the controller gain and the event‐triggering matrix are subsequently provided without using any prior knowledge of the system matrices. Finally, numerical examples are presented to corroborate the merits of the proposed data‐driven event‐triggered control schemes relative to existing results.

Data‐driven analysis and control of periodic event‐triggered continuous‐time systems

“…[19][20][21] The data-based analysis and control for dynamic ETC and self-triggered control systems were also considered. 22,23 It is known that the detecting interval needs to be chosen in advance for different triggering parameters in PETC. However, on the one hand, selecting too small a value of the detecting interval will lead to a high detection frequency of the event-triggering mechanism, which may result in an increase in the number of successful triggers due to the presence of random disturbance, 24 and may further increase the computation burden of the zero-order holder (ZOH) operator.…”

Data‐driven control for networked systems with multiple packet dropouts

“…This Assumption follows from Reference 27. It provides the bound on the noise matrix  − in a form of quadratic matrix inequality, which can capture significant priori knowledge about the system, and similar descriptions of noise bounds have been used inReferences 27,29,[31][32][33][34] The noise model in Assumption 1 is able to describe, for example,…”