Phase synchronization control of complex networks of Lagrangian systems on adaptive digraphs

Chung, Soon‐Jo; Bandyopadhyay, Saptarshi; Chang, Insu; Hadaegh, Fred Y.

doi:10.1016/j.automatica.2013.01.048

Cited by 88 publications

(112 citation statements)

References 37 publications

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“…If a linear diffusive coupling is used, τ i would produce L similar to (4). The EulerLagrange equations appear routinely robotics in the study of rigid body motions of manipulators [26], [27] and spacecraft or aircraft (SE(3)), which have attitude dynamics on SO(3) [28]- [30] and often times have articulated wings [14], [31], [32], appendages, or manipulators attached:…”

Section: Physics-based Models For Robotic Agentsmentioning

confidence: 99%

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: Physics-based Models For Robotic Agentsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Flocking dynamics described by Reynolds' rule are known to be asymptotically stable under fairly weak conditions on the topology of the underlying graph [17], [18], [24], [25]. Stronger results, such as exponential stability, have been found for linear time-varying consensus protocol for single integrator systems [26], in the context of synchronization for second-order Euler-Lagrange systems [20]- [22], and using tools from dynamical systems theory for time-invariant, undirected graph topologies in second-order, two-dimensional (2-D) flocking dynamics [27]. Preliminary results on exponential stability of flocks under tree and star topology constraints are presented in [8].…”

Section: A Overview Of the Literaturementioning

confidence: 94%

“…The term f (x, v) is zero when the graph is undirected (since 1 n B c = 0) or if it has synchronized to a steady state configuration from Section III. This would occur naturally when the flock synchronizes on a faster time scale than the dynamics of its CG [21], [22]. Since we need the flock to point in the direction of the herding target point, it suffices to ensure that its velocity normal to an axis pointing towards x div is driven to zero.…”

Section: A Formulation Of the Herding Problemmentioning

confidence: 99%

“…Over the years, several distributed, scalable coordination algorithms have been developed while trying to obtain mathematical guarantees on the stability of the formation [16]- [22]. This has been accompanied by parallel work on understanding the presence of order and phase transitions in flocks [23], building upon analogies between flocks on the one hand, and fluid and magnetic systems on the other.…”

Section: A Overview Of the Literaturementioning

confidence: 99%

“…In addition to stability, for the purpose of herding, it is useful to have the property of time-scale separation between the synchronization of the flocking dynamics to S(x 0 ) on the one hand, and the translational dynamics of the center of gravity of the flock (whose time constant, as shown in Section V, is 1/k g ) on the other [21], [22]. This can be achieved by a suitable choice of k s and k a for a given graph topology.…”

Section: Stability Analysismentioning

confidence: 99%

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Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle

et al. 2018

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Abstract-In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the mwaypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds' rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations.

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Random adaptive control for cluster synchronization of complex networks with distinct communities

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Park

Lee

et al. 2015

Adaptive Control & Signal

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SummaryIn this paper, we investigate the cluster synchronization for complex networks with time‐varying delayed couplings, stochastic disturbance, and non‐identical nodes in different clusters. Based on randomly occurring controllers, some Bernoulli stochastic variables are introduced to describe the controllers, then, a fraction of nodes in clusters, which have direct connections to the other clusters, is controlled, and the states of the whole dynamical networks can be globally forced to the objective cluster states. Sufficient conditions are derived to guarantee the realization of the mean square cluster synchronization pattern for all initial values by means of Lyapunov stability theory, It differential formula, and LMI approach. Besides, by designing the randomly occurring adaptive update law, some suitable control gains are obtained. Finally, numerical simulations are also given to demonstrate the effectiveness and validity of the main result. Copyright © 2015 John Wiley & Sons, Ltd.

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Phase synchronization control of complex networks of Lagrangian systems on adaptive digraphs

Cited by 88 publications

References 37 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle

Random adaptive control for cluster synchronization of complex networks with distinct communities

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