Swarm stability of high-order linear time-invariant swarm systems

Cai, Ning; Xi, Junhua; Zhong, Yisheng

doi:10.1049/iet-cta.2009.0589

Cited by 73 publications

(86 citation statements)

References 24 publications

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“…Besides introducing a novel method for clustering, a major contribution of this paper is proposing a criterion for checking whether or not a high-order LTI (Linear TimeInvariant) multiagent system can reach group consensus, which generalizes the existing well-known necessary and sufficient condition for consensus achievement [24][25][26][27][28]. In fact, the existing condition on consensus is a particular instance of the condition for group consensus presented here.…”

Section: Complexitymentioning

confidence: 99%

See 1 more Smart Citation

A Novel Clustering Method Based on Quasi-Consensus Motions of Dynamical Multiagent Systems

2017

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This paper presents a novel approach for clustering, which is based on quasi-consensus of dynamical linear high-order multiagent systems. The graph topology is associated with a selected multiagent system, with each agent corresponding to one vertex. In order to reveal the cluster structure, the agents belonging to a similar cluster are expected to aggregate together. To establish the theoretical foundation, a necessary and sufficient condition is given to check the achievement of group consensus. Two numerical instances are furnished to illustrate the results of our approach.

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Section: Complexitymentioning

confidence: 99%

“…, }) are not Hurwitz, then there should be no agreement at all. Actually, the existing well-known criterion for checking the consensus [24][25][26][27][28] (summarized as Lemma 6 here) can be regarded as a particular case or corollary of Theorem 7 when = 2.…”

Section: Remarkmentioning

confidence: 99%

A Novel Clustering Method Based on Quasi-Consensus Motions of Dynamical Multiagent Systems

2017

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“…Topics covered in this paper are related mainly to control and stabilization of MAV teams. In literature, one can find papers describing control methodologies for swarms of both autonomous ground vehicles [13], [14], [15], [16] and unmanned aerial vehicles [17], [18], [19], [20]. These methods are often inspired by nature (e.g., by flocks of birds [21] or molecules forming crystals [22]), and they try to fulfil various requirements of swarm robotics.…”

Section: A Swarms Of Autonomous Vehiclesmentioning

confidence: 99%

“…Considerations influencing the fault tolerance of teams are discussed in [24] and various co-operation strategies for teams of MAVs solving multi-robot tasks are published in [25]. Finally, controllers for swarms of robots with limited communication requirements are described in [14] and [15], where the necessary conditions for swarm stability are described using a direct graph topology in [14], and a Lyapunov-like function is employed for convergence analysis of multi-robot systems in [15].…”

Section: A Swarms Of Autonomous Vehiclesmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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“…In Ren and Beard (2008), Sun and Long (2009) consensus problems for agents with single/double or higher integrator dynamics were studied. In Fax and Murray (2004), the authors focused on the stabilisation of a network of identical agents with linear dynamics whilst in Cai et al (2011) the authors suggested necessary and sufficient conditions for swarm stability of high-order linear time-invariant (LTI) multi-agent systems. Swarm-stabilisation problems for high-order LTI singular multi-agent systems with homogeneous agents was investigated in Xi et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

Kladis

Menon

Edwards

2015

International Journal of Systems Science

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This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of UAVs, modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi-Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly feedback gains are synthesised using a Parallel Distributed Compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as Linear Matrix Inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.

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Swarm stability of high-order linear time-invariant swarm systems

Cited by 73 publications

References 24 publications

A Novel Clustering Method Based on Quasi-Consensus Motions of Dynamical Multiagent Systems

A Novel Clustering Method Based on Quasi-Consensus Motions of Dynamical Multiagent Systems

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

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