Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment

Ögren, Petter; Fiorelli, E.; Leonard, Naomi Ehrich

doi:10.1109/tac.2004.832203

Cited by 1,088 publications

(591 citation statements)

References 31 publications

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“…The precision matrix Q is defined by Q Σ −1 . The objective function f (S t ) can be overhead than global synchronization, which requires all nodes to agree on a common time index, and hence is a common assumption in sensor networks [99]. Furthermore, nodes in S t do not send state values at any iteration, and are therefore not incorporated in state updates.…”

Section: Sensing Model and Objective Function Definitionmentioning

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: Sensing Model and Objective Function Definitionmentioning

confidence: 99%

Submodularity in Dynamics and Control of Networked Systems

Clark

Alomair

Bushnell

et al. 2016

Communications and Control Engineering

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“…Particularly drive-bywire and fly-by-wire systems have shown a high penetration rate in these industries. In wireless NCSs, the communication relies on the wireless technology and it has been finding applications in a broad range of areas that in which it is difficult or expensive to install wire, such as mobile sensor networks [17], HVAC systems [1], automated highway and unmanned aerial vehicles [18].…”

Section: Introductionmentioning

confidence: 99%

Implementation Considerations For Wireless Networked Control Systems

Naghshtabrizi

Hespanha²

2010

Wireless Networking Based Control

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We show that delay impulsive systems are a natural framework to model wired and wireless NCSs with variable sampling intervals and delays as well as packet dropouts. Then, we employ discontinuous Lyapunov functionals to characterize admissible sampling intervals and delays such that exponential stability of NCS is guaranteed. These results allow us to determine requirements needed to establish exponential stability, which is the most basic Quality of Performance (QoP) required by the application layer. Then we focus on the question of whether or not the Quality of Service (QoS) provided by the wireless network suffices to fulfil the required QoP. To answer this question we employ results from real-time scheduling and provide a set of conditions under which the desired QoP can be achieved.

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“…A key component of active sensing systems is the dependence of information-gathering on robot motion and the ability to predict in advance the effect of robot motion on the quality of information that is collected [14]- [16]. For broad area sensing tasks such as exploration and surveillance, information is often described in terms of coverage area.…”

Section: Introductionmentioning

confidence: 99%

“…The main drawback of this category of algorithms is failure to consider uncertainty in sensor information. The second category of algorithms, often referred to as adaptive sampling, focus on the directed sampling of a scalar field in order to obtain certain statistical properties or to locate sources, contours, and boundaries [5], [16]- [18]. The main drawback of these adaptive sampling approaches is their reliance on local information only and failure to develop and consider models of the underlying phenomena being measured.…”

Section: Introductionmentioning

confidence: 99%

Information-theoretic integration of sensing and communication for active robot networks

Frew¹

2007

The Proceedings of First International Conference on Robot Communication and Coordination

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Abstract-This paper presents an information-theoretic approach to sensor placement that incorporates communication capacity into an optimal formulation. A new formulation is presented that maximizes the information rate achievable by a set of sensors communicating wirelessly to a single collection node. Shannon capacity and the standard radio propagation model are used to model the throughput achievable by a sensor configuration. Likewise, the d-optimality criterion from the active sensing literature is used to model information gain provided by range and bearing sensors. The combiniation of informationtheoretic measures leads to a metric equivalent to the expected information rate achievable by the system. Sensor positions are selected that optimize this measure.

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Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment

Cited by 1,088 publications

References 31 publications

Submodularity in Dynamics and Control of Networked Systems

Submodularity in Dynamics and Control of Networked Systems

Implementation Considerations For Wireless Networked Control Systems

Information-theoretic integration of sensing and communication for active robot networks

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