Assessment of event-triggered policies of nonpharmaceutical interventions based on epidemiological indicators.

Nonpharmaceutical interventions (NPI) such as banning public events or instituting lockdowns have been widely applied around the world to control the current COVID-19 pandemic. Typically, this type of intervention is imposed when an epidemiological indicator in a given population exceeds a certain threshold. Then, the nonpharmaceutical intervention is lifted when the levels of the indicator used have decreased sufficiently. What is the best indicator to use? In this paper, we propose a mathematical framework to try to answer this question. More specifically, the proposed framework permits to assess and compare different event-triggered controls based on epidemiological indicators. Our methodology consists of considering some outcomes that are consequences of the nonpharmaceutical interventions that a decision maker aims to make as low as possible. The peak demand for intensive care units (ICU) and the total number of days in lockdown are examples of such outcomes. If an epidemiological indicator is used to trigger the interventions, there is naturally a trade-off between the outcomes that can be seen as a curve parameterized by the trigger threshold to be used. The computation of these curves for a group of indicators then allows the selection of the best indicator the curve of which dominates the curves of the other indicators. This methodology is illustrated with indicators in the context of COVID-19 using deterministic compartmental models in discrete-time, although the framework can be adapted for a larger class of models.

Periodic Event-Triggered Control strategy for a (3,0) mobile robot network.

This work deals with the development of a nonlinear Periodic Event-Triggered Control strategy employed to the consensus of a multi-vehicle autonomous system based on (3,0) mobile robots. First, the existence of the Control Lyapunov Function (CLF) applicable to the consensus problem is proven. This is subsequently used to develop event and feedback functions. The Periodic Event-Triggered Control ensures trajectories boundedness and convergence to consensus while a specific sampling period is provided. Also, the formation problem is addressed as an extension of the presented work. Experimental results show the performance of the proposed control strategy which reduces 99.78% the number of control updates compared to a continuous control law, resulting in energy saving for the information transfer from central control to the mobile robots.

Leader-Following Consensus and Formation Control of VTOL-UAVs with Event-Triggered Communications †.

This article presents the design and implementation of an event-triggered control approach, applied to the leader-following consensus and formation of a group of autonomous micro-aircraft with capabilities of vertical take-off and landing (VTOL-UAVs). The control strategy is based on an inner-outer loop control approach. The inner control law stabilizes the attitude and position of one agent, whereas the outer control follows a virtual leader to achieve position consensus cooperatively through an event-triggered policy. The communication topology uses undirected and connected graphs. With such an event-triggered control, the closed-loop trajectories converge to a compact sphere, centered in the origin of the error space. Furthermore, the minimal inter-sampling time is proven to be below bounded avoiding the Zeno behavior. The formation problem addresses the group of agents to fly in a given shape configuration. The simulation and experimental results highlight the performance of the proposed control strategy.