Containment in leader–follower networks with switching communication topologies

Notarstefano, Giuseppe; Egerstedt, Magnus; Haque, Musad

doi:10.1016/j.automatica.2011.01.077

Cited by 265 publications

(155 citation statements)

References 13 publications

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“…The connection between the convergence rate and the spectrum of the network graph has been observed in leader-follower systems [112] and networks without leaders [17]. The problem of containment, defined as guaranteeing that the follower node states converge to the convex hull of the leader node states, was studied for static networks in [66] and dynamic networks in [98].…”

Section: Minimizing Convergence Errormentioning

confidence: 99%

Submodularity in Dynamics and Control of Networked Systems

Clark

Alomair

Bushnell

et al. 2016

Communications and Control Engineering

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Submodularity in Dynamics and Control of Networked Systems Andrew ClarkCo-Chairs of the Supervisory Committee:Radha Poovendran Electrical Engineering Linda Bushnell Electrical EngineeringControlling a networked dynamical system to reach a desired state is a fundamental challenge in applications including transportation, energy, social, and biological systems. One scalable approach to controlling such systems is to directly control the states of a subset of leader nodes, while relying on local interactions to steer the remaining nodes towards their desired states. The choice of leader nodes is known to affect system metrics including robustness to noise, rate of convergence to a desired state, and controllability of the system.Selecting an optimal subset of leader nodes, however, is inherently a combinatorial problem, making optimal leader node selection intractable in general.This thesis presents a submodular optimization framework to selecting leader nodes for control of networked systems. We investigate the problem of selecting a subset of leader nodes in order to minimize node state errors due to noise in the communication links between nodes. We prove that the error due to link noise is a supermodular function of the set of leader nodes, leading to the first polynomial-time algorithms for minimizing error due to link noise with provable optimality bounds. We develop our approach for networks with static topologies, as well as dynamic topologies due to random link failures, switching between predefined topologies, and arbitrary mobility.We study selecting leader nodes in order to minimize convergence error, defined as the error in the intermediate node states prior to reaching their desired values. We derive upper bounds for a class of convergence error metrics based on the hitting time of random walk on the network, which we prove to be a supermodular function of the set of input nodes.We present polynomial-time algorithms for minimizing convergence error with provable optimality bounds, for static as well as dynamic networks.Efficient algorithms have recently been proposed for selecting leader nodes to satisfy controllability, defined as the ability to drive the non-input nodes from any initial state to any desired state in finite time. These algorithms, however, do not incorporate performance criteria including robustness to noise and convergence rate. We study the problem of leader selection for joint performance and controllability, and prove that controllability can be formulated as a matroid constraint on the set of leader nodes. We propose efficient algorithms with provable optimality gap for selecting leader nodes for joint performance and controllability, and characterize the submodular structure of the largest controllable subgraph of a network.We also investigate selecting input nodes for guaranteeing synchronization in networked systems. Using the widely-studied Kuramoto model of nonlinear phase-coupled oscillators, we develop novel threshold-based conditions for a set of input nodes to ...

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Section: Minimizing Convergence Errormentioning

confidence: 99%

Submodularity in Dynamics and Control of Networked Systems

Clark

Alomair

Bushnell

et al. 2016

Communications and Control Engineering

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“…record can be done by each UAV itself but circle needs two assisting actions hcirclea and hcircleb from two other UAVs. On the other hand, for each UGV a k,i ∈ g k , there are 3 storage areas S 1 , S 2 , S 3 with 3 different objects of interest o [1,20]. They are labeled by the action names shorten by replacing "pick" with "p", "drop" with "d", "circle" with "c" and "record" with "r".…”

Section: Case Studymentioning

confidence: 99%

“…The case where these local tasks are fully independent is considered in [12] for a partially-known workspace. Additional relative-motion constraints among neighboring agents such as relative-distance and connectivity maintenance constraints are addressed in [3], [20].…”

Section: Introductionmentioning

confidence: 99%

Task and Motion Coordination for Heterogeneous Multiagent Systems With Loosely Coupled Local Tasks

Guo

Dimarogonas

2017

IEEE Trans. Automat. Sci. Eng.

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We consider a multiagent system that consists of heterogeneous groups of homogeneous agents. Instead of defining a global task for the whole team, each agent is assigned a local task as syntactically cosafe linear temporal logic formulas that specify both motion and action requirements. Interagent dependence is introduced by collaborative actions, of which the execution requires multiple agents' collaboration. To ensure the satisfaction of all local tasks without central coordination, we propose a bottom-up motion and task coordination strategy that contains an off-line initial plan synthesis and an online coordination scheme based on real-time exchange of request and reply messages. It facilitates not only the collaboration among heterogeneous agents but also the task swapping between homogeneous agents to reduce the total execution cost. It is distributed as any decision is made locally by each agent based on local computation and communication within neighboring agents. It is scalable and resilient to agent failures as the dependence is formed and removed dynamically based on agent capabilities and their plan execution status, instead of preassigned agent identities. The overall scheme is demonstrated by a simulated scenario of 20 agents with loosely coupled local tasks.

the Swedish Research Council (VR)

H2020 ERC Starting Grant BUCOPHSYS

EU STREP RECONFIG: FP7-ICT-2011-9-600825.

QC 20170614

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“…Consider a network consisting of four followers and two leaders, whose dynamics are described by (2) and ( …”

Section: Simulationmentioning

confidence: 99%

“…Containment control of multi-agent systems has received considerable attention due to its potential applications, such as earth monitoring, stellar observation for satellite formation 1 , removing hazardous materials for autonomous robots 2 , and so on. Containment control problem arises in the presence of multiple leaders, where its objective is to drive the followers into the convex hull spanned by the leaders.…”

Section: Introductionmentioning

confidence: 99%

Containment Control for Nonlinear Multi-agent Systems with Parameter Uncertainties and Communication Delays

Wang

Jia

2017

JRNAL

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This paper considers robust containment control problem for uncertain multi-agent systems with inherent nonlinear dynamics. A distributed protocol is proposed using local delayed state information, and then the original problem is converted into an H  control problem by defining an appropriate controlled output function. Based on robust H  theory, sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to ensure the H  containment. A numerical example is provided to demonstrate the effectiveness of our theoretical results.

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Containment in leader–follower networks with switching communication topologies

Cited by 265 publications

References 13 publications

Submodularity in Dynamics and Control of Networked Systems

Submodularity in Dynamics and Control of Networked Systems

Task and Motion Coordination for Heterogeneous Multiagent Systems With Loosely Coupled Local Tasks

Containment Control for Nonlinear Multi-agent Systems with Parameter Uncertainties and Communication Delays

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