Broadcast control of multi-agent systems

Azuma, Shun‐ichi; Yoshimura, Ryota; Sugie, Toshiharu

doi:10.1016/j.automatica.2013.04.022

Cited by 60 publications

(29 citation statements)

References 13 publications

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“…al. [2] and others, since the agents are unable to obey global-direction-based commands. Research methods that draw inspiration from animal behaviour in herds in nature e.g.…”

Section: Controlling Multi-agent Systems -The Ideamentioning

confidence: 99%

On Steering Swarms

Barel

Manor

Bruckstein

2018

Lecture Notes in Computer Science

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The main contribution of this paper is a novel method allowing an external observer/controller to steer and guide swarms of identical and indistinguishable agents, in spite of the agents' lack of information on absolute location and orientation. Importantly, this is done via simple global broadcast signals, based on the observed average swarm location, with no need to send control signals to any specific agent in the swarm.

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“…al. [2] and others, since the agents are unable to obey global-direction-based commands. Research methods that draw inspiration from animal behaviour in herds in nature e.g.…”

Section: Controlling Multi-agent Systems -The Ideamentioning

confidence: 99%

On Steering Swarms

Barel

Manor

Bruckstein

2018

Lecture Notes in Computer Science

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“…The strategy of stabilization by noise is used for several particular control problems, for example, settling problems of inverted pendulums by adding high-frequency oscillation on drive joints [8], mechanical system control problems by using dither signals [9,10], broadcast control problems of multi-agent systems based on stochastic signals [11], and asymptotic stabilization problems of nonholonomic systems by state-feedbacks with Gaussian white noises [7,12].…”

Section: Introductionmentioning

confidence: 99%

Stabilization by unbounded‐variation noises

Nishimura

2016

Intl J Robust & Nonlinear

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In this paper, we claim the availability of deterministic noises for stabilization of the origins of dynamical systems, provided that the noises have unbounded variations. To achieve the result, we first consider the system representations of rough systems based on rough path analysis; then, we provide the notion of asymptotic stability for rough systems to analyze the stability for the systems. In the procedure, we also confirm that the system representations include stochastic differential equations; we also found that asymptotic stability for rough systems is the same property as uniform almost sure asymptotic stability provided by Bardi and Cesaroni. After the discussion, we confirm that there is a case that deterministic noises are capable of making the origin become asymptotically stable for rough systems while stochastic noises do not achieve the same stabilization results. ‡ In this paper, the term 'noises' is used when deterministic noises and stochastic noises are stated in a bundle. The deterministic noises are non-probabilistic signals having high-frequency oscillations, and the stochastic noises are probabilistic vibration signals such as Wiener processes.STABILIZATION BY UNBOUNDED-VARIATION NOISES 4127 subsets of Euclidean spaces. The stochastic stability property 'almost the same' as AS is uniform almost sure asymptotic stability (UASAS) [13,14]; all the sublevel sets of the related Lyapunov functions are invariant sets with probability one. However, achieving the property is generally difficult because the necessary and sufficient conditions are restrictive. Furthermore, there is a negative result that the non-AS origin never becomes the UASAS origin by the addition of any diffusion term [15]; that is, as long as employing the stabilization by noise with the use of Wiener processes, we should allow the possibility of all sample paths traveling to points very far away from the origin. Nevertheless, this paper claims that the stabilization by noise has still possibility to provide the non-AS origins with AS, provided that the resulting systems are neither ordinary nor stochastic differential equations. The assertion will be confirmed by the addition of deterministic noises having unbounded variations; this plan needs to employ a system representation by using rough path analysis [16][17][18][19] because the theory enables considering the dynamics with unbounded-variation functions such as Wiener processes and a particular kind of deterministic processes. The key point of the analysis is to classify external inputs by calculating the orders of the variations that is finite. Based on the information of the orders, rough paths and their dynamics-generally said to be rough differential equations, and rough systems in this paper-are obtained.Recently in [20], the stability notions for rough systems are defined with some important discussions; however, the claims have not been proven completely. Therefore, in this paper, we provide the proof of a concrete system representation of rough systems, show that ...

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“…Also, it has been applied to a wide range of engineering problems, e.g. , model‐free control , adaptive control , image registration , neural networks , and multi‐agent control .…”

Section: Introductionmentioning

confidence: 99%

“…For instance, to enhance its applicability, the original algorithm has been extended to the one-measurement version [2], the adaptive version [3], the global version [4], the one-sided version [5], and so on (see [6] for other variations). Also, it has been applied to a wide range of engineering problems, e.g., model-free control [7][8][9], adaptive control [10][11][12], image registration [13][14][15], neural networks [16][17][18], and multi-agent control [19,20].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Perturbation Stochastic Approximation with Norm‐Limited Update Vector

Tanaka¹,

Azuma²,

Sugie³

2015

Asian Journal of Control

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This paper addresses the convergence of simultaneous perturbation stochastic approximation (SPSA) with a norm‐limited update vector. We first illustrate an unstable solution of the standard SPSA algorithm which motivates the consideration of a modified version, where the norm of the update vector is limited to a certain value. Next, a result on the almost‐sure convergence is presented by reducing the modified algorithm into the standard SPSA algorithm and restricting the probability distribution for the perturbation to a Bernoulli distribution. Finally, we apply the modified algorithm to a system identification problem to demonstrate its performance.

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Broadcast control of multi-agent systems

Cited by 60 publications

References 13 publications

On Steering Swarms

On Steering Swarms

Stabilization by unbounded‐variation noises

Simultaneous Perturbation Stochastic Approximation with Norm‐Limited Update Vector

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