Graph Process Specifications for Hybrid Networked Systems

©2012 Springer. The original publication is available at www.springerlink.com: http://dx.doi.org/10.1007/s10626-012-0134-2DOI: 10.1007/s10626-012-0134-2Many large-scale multi-agent missions consist of a sequence of subtasks, each of which can be accomplished separately by having agents execute appropriate decentralized controllers. However, many decentralized controllers have network topological prerequisites that must be satisfied in order to achieve the desired effect on a system. Therefore, one cannot always hope to accomplish the original mission by having agents naively switch through executing the controllers for each subtask. This paper extends the Graph Process Specification (GPS) framework, which was presented in previous work as a way to script decentralized control sequences for agents, while ensuring that network topological requirements are satisfied when each controller in the sequence is executed. Atoms, the fundamental building blocks in GPS, each explicitly state a network topological transition. Moreover, they specify the means to make that transition occur by providing a multi-agent controller, as well as a way to locally detect the transition. Scripting a control sequence in GPS therefore reduces to selecting a sequence of atoms from a library to satisfy network topological requirements, and specifying interrupt conditions for switching. As an example of how to construct an atom library, the optimal decentralization algorithm is used to generate atoms for agents to track desired multi-agent motions with when the network topology is static. The paper concludes with a simulation of agents performing a drumline-inspired dance using decentralized controllers generated by optimal decentralization and scripted using GPS

A transient homotypic interaction model for the influenza A virus NS1 protein effector domain

Influenza A virus NS1 protein is a multifunctional virulence factor consisting of an RNA binding domain (RBD), a short linker, an effector domain (ED), and a C-terminal 'tail'. Although poorly understood, NS1 multimerization may autoregulate its actions. While RBD dimerization seems functionally conserved, two possible apo ED dimers have been proposed (helix-helix and strand-strand). Here, we analyze all available RBD, ED, and full-length NS1 structures, including four novel crystal structures obtained using EDs from divergent human and avian viruses, as well as two forms of a monomeric ED mutant. The data reveal the helix-helix interface as the only strictly conserved ED homodimeric contact. Furthermore, a mutant NS1 unable to form the helix-helix dimer is compromised in its ability to bind dsRNA efficiently, implying that ED multimerization influences RBD activity. Our bioinformatical work also suggests that the helix-helix interface is variable and transient, thereby allowing two ED monomers to twist relative to one another and possibly separate. In this regard, we found a mAb that recognizes NS1 via a residue completely buried within the ED helix-helix interface, and which may help highlight potential different conformational populations of NS1 (putatively termed 'helix-closed' and 'helix-open') in virus-infected cells. 'Helix-closed' conformations appear to enhance dsRNA binding, and 'helix-open' conformations allow otherwise inaccessible interactions with host factors. Our data support a new model of NS1 regulation in which the RBD remains dimeric throughout infection, while the ED switches between several quaternary states in order to expand its functional space. Such a concept may be applicable to other small multifunctional proteins

Critical Role of Constitutive Type I Interferon Response in Bronchial Epithelial Cell to Influenza Infection

Innate antiviral responses in bronchial epithelial cells (BECs) provide the first line of defense against respiratory viral infection and the effectiveness of this response is critically dependent on the type I interferons (IFNs). However the importance of the antiviral responses in BECs during influenza infection is not well understood. We profiled the innate immune response to infection with H3N2 and H5N1 virus using Calu-3 cells and primary BECs to model proximal airway cells. The susceptibility of BECs to influenza infection was not solely dependent on the sialic acid-bearing glycoprotein, and antiviral responses that occurred after viral endocytosis was more important in limiting viral replication. The early antiviral response and apoptosis correlated with the ability to limit viral replication. Both viruses reduced RIG-I associated antiviral responses and subsequent induction of IFN-β. However it was found that there was constitutive release of IFN-β by BECs and this was critical in inducing late antiviral signaling via type I IFN receptors, and was crucial in limiting viral infection. This study characterizes anti-influenza virus responses in airway epithelial cells and shows that constitutive IFN-β release plays a more important role in initiating protective late IFN-stimulated responses during human influenza infection in bronchial epithelial cells

Control of multi-agent networks: from network design to decentralized coordination

This dissertation presents a suite of design tools for multi-agent systems that address three main areas: network design, decentralized controller generation, and the synthesis of decentralized control strategies by combining individual decentralized controllers. First, a new metric for quantifying heterogeneity in multi-agent systems is presented based on combining different notions of entropy, and is shown to overcome the drawbacks associated with existing diversity metrics in various scientific fields. Moreover, a new method of controlling multi-agent networks through the single-leader network paradigm is presented where by directly exploiting the homogeneity of agent capabilities, a network which is not completely controllable can be driven closer to a desired target configuration than by using traditional control techniques. An algorithm is presented for generating decentralized control laws that allow for agents to best satisfy a desired global objective, while taking into account network topological constraints and limitations on how agents can compute their control signals. Then, a scripting tool is developed to aid in specifying sequences of decentralized controllers to be executed consecutively, while helping ensure that the required network topological requirements needed for each controller to execute properly are maintained throughout mode switches. Finally, the underlying concepts behind the developed tools are showcased in three example applications: distributed merging and spacing for heterogeneous aircraft during terminal approaches, collaborative multi-UAV convoy protection in dynamic environments, and an educational tool used to teach a graduate-level networked controls course at the Georgia Institute of Technology.PhDCommittee Chair: Egerstedt, Magnus; Committee Member: Feron, Eric; Committee Member: Hong, Bo; Committee Member: Shamma, Jeff; Committee Member: Wardi, Yora

Distributed scheduling for heterogeneous air traffic merging and spacing during the terminal phase of flight

Issued as final reportRockwell Collin

Optimal Decentralization of Multi-Agent Motions

© 2010 AACCPresented at the 2010 American Control Conference Marriott Waterfront, Baltimore, MD, USA; June 30-July 02, 2010This paper addresses how to optimally decentralize the execution of a multi-agent mission defined at the trajectory-level, where the information flow among agents in the system are limited by a predefined network topology. Each agent's decentralized controllers are constrained to be parameterized functions of the relative distances and angles between itself and its neighbors. Starting with a discussion on what it means for a controller to be considered decentralized, the problem is posed as an optimal control problem for switched autonomous systems. We derive optimality conditions for the parameters defining each mode for each agent, which is combined with optimality conditions for when to switch between consecutive modes. Simulations are used to showcase the operation of the proposed optimal decentralization algorithm on a complex example

A Measure of Heterogeneity in Multi-Agent Systems

Abstract — Heterogeneous multi-agent systems have previously been studied and deployed to solve a number of different tasks. Despite this, we still lack a basic understanding of just what “heterogeneity ” really is. For example, what makes one team of agents more heterogeneous than another? In this paper, we address this issue by proposing a measure of heterogeneity. This measure takes both the complexity and disparity of a system into account by combining different notions of entropy. The result is a formulation that is both easily computable and makes intuitive sense. An overview is given of existing metrics for diversity found in various fields such as biology, economics, as well as robotics, followed by a discussion of their relative merits and demerits. We show how our proposed measure of heterogeneity overcomes problematic issues identified across the previous metrics. Finally, we discuss how to apply the new measure of heterogeneity specifically to multi-agent systems by using the notion of a common task-space to compare agents with different capabilities. I

Multi-Robot Search and Rescue: An Open-Ended Educational Bridge Between Theory and Practice

Presented at the First Workshop on Cyber-Physical Systems Education (CPS-Ed 2013), at Cyber Physical Systems Week (CPSWeek 2013), Philadelphia, Pennsylvania, USA, April 2013.This paper reports on a multi-robot search and rescue final project that has been used at the Georgia Institute of Technology to educate students on how to methodically apply networked control theory concepts towards solving complex cyberphysical system (CPS) engineering problems. For the project, students design control laws to coordinate a team of simulated robots in completing a set of mission objectives within a custom developed virtual environment. The virtual environment lets students script high-level algorithms and experience how their computational solutions perform when coupled with both physical constraints and environmental factors, as is often the case in real robotics applications. By allowing certain physical domain effects to be toggled on or off, students learn to iteratively adapt theoretical solutions based on simplified mathematical models to obtain engineering solutions for complex CPS problems

A Controlled-Precision Algorithm for Mode-Switching Optimization

© 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Presented at the IEEE Conference on Decision and Control, Maui, Hawaii, Dec. 2012.DOI: 10.1109/CDC.2012.6426621This paper describes an adaptive-precision algorithm for solving a general optimal mode-scheduling problem in switched-mode dynamical systems. The problem is complicated by the fact that the controlled variable has discrete and continuous components, namely the sequence of modes and the switching times between them. Recently we developed a gradient-descent algorithm whose salient feature is that its descent at a given iteration is independent of the length (number of modes) of the schedule, hence it is suitable to situations where the schedule-lengths at successive iterations grow unboundedly. The computation of the descent direction requires grid-based approximations to solve differential equations as well as minimize certain functions on uncountable sets. However, the algorithm’s convergence analysis assumes exact computations, and it breaks down when approximations are used, because the descent directions are discontinuous in the problem parameters. The purpose of the present paper is to overcome this theoretical gap and its computational implications by developing an implementable, adaptive-precision algorithm that controls the approximation levels by balancing precision with computational workloads. Its asymptotic convergence is proved, and simulation results are provided to support the theoretical developments

Controllability of Homogeneous Single-Leader Networks

(c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Digital Object Identifier : 10.1109/CDC.2010.5718103This paper addresses an aspect of controllability in a single-leader network when the agents are homogeneous. In such a network, indices are not assigned to the individual agents and controllability, which is typically a point to point property, now becomes a point to set property, where the set consists of all permutations of the target point. Agent homogeneity allows for choice of the optimal target point permutation that minimizes the distance to the system's reachable subspace, which we show is equivalent to finding a minimum sum-of-squares clustering with constraints on the cluster sizes. However, finding the optimal permutation is NP-hard. Methods are presented to find suboptimal permutations in the general case and the optimal permutation when the agent positions are 1-D