High-Order and Model Reference Consensus Algorithms in Cooperative Control of MultiVehicle Systems

Ren, Wei; Moore, Kevin L.; Chen, YangQuan

doi:10.1115/1.2764508

Cited by 383 publications

(234 citation statements)

References 22 publications

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“…As shown in Ren [27,29,30], Olfati-Saber [24,25], Moreau [22,23], and Cao [5], the role of connectivity and stability are highly relevant for cooperative control algorithms. For directed graphs with a fixed topology (i.e.…”

Section: Connection To Consensus Algorithmsmentioning

confidence: 99%

Swarming on Random Graphs

et al. 2013

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We consider a compromise model in one dimension in which pairs of agents interact through first-order dynamics that involve both attraction and repulsion. In the case of all-to-all coupling of agents, this system has a lowest energy state in which half of the agents agree upon one value and the other half agree upon a different value. The purpose of this paper is to study the behavior of this compromise model when the interaction between the N agents occurs according to an Erdős-Rényi random graph G (N, p). We study the effect of changing p on the stability of the compromised state, and derive both rigorous and asymptotic results suggesting that the stability is preserved for probabilities greater than p c = O( log N N ). In other words, relatively few interactions are needed to preserve stability of the state. The results rely on basic probability arguments and the theory of eigenvalues of random matrices.

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Section: Connection To Consensus Algorithmsmentioning

confidence: 99%

Swarming on Random Graphs

et al. 2013

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“…No matter whether the consensus variable is a vector or not, such consensus protocols are in fact first-order, since the typical consensus protocol adjusts the first derivative of the consensus variable for each agent based on the consensus variable of its neighbors. Despite their widespread importance, there has been little work dealing explicitly with higher-order consensus seeking processes on multiagent networks [2,12,18,19]. Under various topological and communication condition settings, these few examples show the convergence conditions that guarantee the second-order or third-order consensus, but provide neither a generic picture nor systematic insight into the connection between the stability of the general high-order consensus and the system parameters such as the network topology and feedback gains.…”

Section: Introductionmentioning

confidence: 99%

High-Order Consensus Seeking: A Frequency Domain Approach

Yang¹

2014

Appl. Math. Inf. Sci.

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High-order consensus, in which individual high-order dynamic units keep in pace with each other in a distributed fashion, depends both on the feedback gains of the protocol and on the properties of the interaction network. By employing a frequency domain method, we explicitly derive analytical equations that clarify a rigorous connection between the stability of general high-order consensus and the system parameters such as the network topology and feedback gains. Using the derived consensus polynomials, the general sufficient and necessary stability criterion is obtained for high-order consensus networks of arbitrary topology. Furthermore, a sufficient condition of desirable time complexity for high-order consensus is given by exploiting the topology properties of underlying networks. Numerical simulation results are provided to demonstrate the effectiveness of our theoretical results.

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“…(13) (Ren, 2007;Ren et al, 2009;Khoo et al, 2009). (1)- (2) are considered in R n , a corresponding version of equation (14) and (16) are the following equations…”

Section: Decentralized Feedback Lawsmentioning

confidence: 99%

Adaptive consensus of multi-agents in networks with jointly connected topologies

2012

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In this paper, the consensus problem of multi-agent following a leader is studied. An adaptive design method is presented for multi-agent systems with non-identical unknown nonlinear dynamics, and for a leader to be followed that is also nonlinear and unknown. By parameterizations of unknown nonlinear dynamics of all agents, a decentralized adaptive consensus algorithm is proposed in networks with jointly connected topologies by incorporating local consensus errors in addition to relative position feedback. Analysis of stability and parameter convergence of the proposed algorithm are conducted based on algebraic graph theory and Lyapunov theory. Finally, examples are provided to validate the theoretical results.

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High-Order and Model Reference Consensus Algorithms in Cooperative Control of MultiVehicle Systems

Cited by 383 publications

References 22 publications

Swarming on Random Graphs

Swarming on Random Graphs

High-Order Consensus Seeking: A Frequency Domain Approach

Adaptive consensus of multi-agents in networks with jointly connected topologies

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