Containment Control in Mobile Networks

Ji, Meng; Ferrari-Trecate, Giancarlo; Egerstedt, Magnus; Buffa, Annalisa

doi:10.1109/tac.2008.930098

Cited by 706 publications

(419 citation statements)

References 16 publications

(24 reference statements)

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“…Despite the apparent simplicity of the model, it is able to capture the possibility of long-range order, as explained later in this section. A generalized representation of the models in [18] and [19] can be obtained by using partial difference equations (PdEs) [20], [21]. The rules for obtaining PdEs permit a natural association with continuum PDEs, and consequently, ways for deriving flocking laws based on PDEs other than the wave equation used in [20].…”

Section: B Models For Swarm Dynamical Systemsmentioning

confidence: 99%

“…An example of adversarial control is the family containment and herding strategies modeled after dolphins [21], sheep-dogs [147]- [152] and birds of prey used to herd a flock of birds [153]. In [153], the authors examined the use of a robotic UAV, possibly one built to resemble a bird of prey like a falcon, to herd flocks of birds away from sensitive areas like airports and solar farms.…”

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

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A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: B Models For Swarm Dynamical Systemsmentioning

confidence: 99%

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…One notion of convergence that has been studied in formation control [66] is containment, which occurs when the follower node states are in the convex hull of the leader node states.…”

Section: Minimizing Convergence Errormentioning

confidence: 99%

“…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%

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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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Adaptive control and signal processing literature survey (No. 6)

2008

Adaptive Control & Signal

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Containment Control in Mobile Networks

Cited by 706 publications

References 16 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Submodularity in Dynamics and Control of Networked Systems

Adaptive control and signal processing literature survey (No. 6)

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