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Summary In this paper, we develop an adaptive control algorithm for addressing security for a class of networked vehicles that comprise a formation of n^ human‐driven vehicles sharing kinematic dat...

Since 2020, COVID-19 has wreaked havoc across the planet, taking the lives of more than one million people. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that can support effective policy-making. In article number 2000277, Agnieszka Truszkowska, Maurizio Porfiri, and co-workers report a high-resolution, agent-based modeling platform to simulate the spreading of COVID-19 in the city of New Rochelle, NY-one of the first outbreaks registered in the United States. Image by Anna Sawulska, Agnieszka Truszkowska, Beata Truszkowska, and Maurizio Porfiri.

Summary The event-based control strategy is an effective methodology for reducing the controller update and communication over the network. In this paper, the event-based consensus of multi-agent systems with linear dynamics and time-varying topology is studied. For each agent, a state-dependent threshold with an exponentially decaying bound is presented to determine the event times, and a new event-based dynamic feedback scheme is proposed. It is shown that the controller update for each agent is only dependent on its own event times, which reduces significantly the controller update or computation for each agent. Moreover, based on the event-based dynamic feedback scheme and the event triggering function presented in this paper, the continuous communication among neighboring agents is avoided, and the Zeno-behavior of the closed-loop systems is excluded. A numerical example is given to illustrate the effectiveness of theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY -- one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches -- in hospitals or drive-through facilities -- and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features., 44 pages (including 16 of Supplementary Information). Published online in Advanced Theory and Simulations

In this paper, we propose two asynchronous distributed protocols for the heading consensus of a multi-agent group which cannot access a global coordinate system and a global time. Both the leaderless and leader-following cases are addressed, and inter-agent communication delay is taken into account. It is proved, under some standard connectivity assumptions, that our leaderless algorithm ensures the heading consensus provided the initial headings are not balanced; and the leader-based algorithm guarantees the global heading consensus.

SUMMARY In this paper, distributed leader–follower control algorithms are presented for linear multi-agent systems based on output regulation theory and internal model principle. By treating a leader to be followed as an exosystem, the proposed framework can be used to generalize existing multi-agent coordination solutions to allow the identical agents to track an active leader with different dynamics and unmeasurable variables. Moreover, the obtained results for multi-agent coordination control are an extension of previous work on centralized and decentralized output regulation to a distributed control context. Necessary and sufficient conditions for the distributed output regulation problem are given. Finally, distributed output regulation of some classes of multi-agent systems with switching interconnection topologies are discussed via both static and dynamic feedback. Copyright © 2011 John Wiley & Sons, Ltd.

SUMMARY New small-gain results are obtained for nonlinear feedback systems under relaxed assumptions. Specifically, during a transient period, the solutions of the feedback system may not satisfy some key inequalities that previous small-gain results usually utilize to prove stability properties. The results allow the application of the small-gain perspective to various systems that satisfy less-demanding stability notions than the input-to-output stability property. The robust global feedback stabilization problem of an uncertain time-delayed chemostat model is solved by means of the new small-gain results. Copyright © 2011 John Wiley & Sons, Ltd.

We are concerned with the problem of regulating the equilibrium point of a non-linear system in the presence of both parametric and dynamic uncertainties. For the parametric uncertainty we propose a new adaptive controller based on a Lyapunov design and guaranteeing the global boundedness of the solution if a growth condition is satisfied. For the dynamic uncertainty we propose a new way of characterizing the unmodelled effects which encompasses some singular and regular perturbations as illustrated by our worked example. Finally we show how, by modifying the above controller, the boundedness property can be made robust to these unmodelled effects.