Hybrid Controllers for Systems with Random Communication Delays

Seshadhri, Subathra; Ayyagari, Ramakalyan

doi:10.1109/artcom.2009.143

Cited by 7 publications

(8 citation statements)

References 22 publications

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“…Then the above theorem says that the rest of the large part (i.e., 3 Note that [KPS13] claimed that the number of modified edges is at most O( φ log n εn) and the maximum degree of the resulting graph is d + O(⌈ dφ 2 log n ⌉). However, this claim is not correct (at least for d-bounded graphs with d being constant), and the number of changed edges and the maximum degree bound from their analysis should be O( ε φ n) and d + 16, respectively [Ses19]. They obtained their claimed results by adding t := ⌈ dφ 2 c log n ⌉ parallel edges while repairing bad vertices, from which they get that the maximum degree is d + 16t and the number of added edges to the optimal distance (i.e., εdn) is 16t dφ = O(φ/ log n), which is incorrect as it always holds that t = 1 for constant d and large enough n. clusterable part) exhibits some nice local mixing property: a typical uniform averaging random walk (of appropriately chosen length) from a large cluster (of size Ω( √ εn)) will converge quickly to the uniform distribution on it.…”

Section: The Number Of Edges Changed Is At Mostmentioning

confidence: 99%

Robust Clustering Oracle and Local Reconstructor of Cluster Structure of Graphs

Pan¹

2020

Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms

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Due to the massive size of modern network data, local algorithms that run in sublinear time for analyzing the cluster structure of the graph are receiving growing interest. Two typical examples are local graph clustering algorithms that find a cluster from a seed node with running time proportional to the size of the output set, and clusterability testing algorithms that decide if a graph can be partitioned into a few clusters in the framework of property testing.In this work, we develop sublinear time algorithms for analyzing the cluster structure of graphs with noisy partial information. By using conductance based definitions for measuring the quality of clusters and the cluster structure, we formalize a definition of noisy clusterable graphs with bounded maximum degree. The algorithm is given query access to the adjacency list to such a graph. We then formalize the notion of robust clustering oracle for a noisy clusterable graph, and give an algorithm that builds such an oracle in sublinear time, which can be further used to support typical queries (e.g., ISOUTLIER(s), SAMECLUSTER(s, t)) regarding the cluster structure of the graph in sublinear time. All the answers are consistent with a partition of G in which all but a small fraction of vertices belong to some good cluster. We also give a local reconstructor for a noisy clusterable graph that provides query access to a reconstructed graph that is guaranteed to be clusterable in sublinear time. All the query answers are consistent with a clusterable graph which is guaranteed to be close to the original graph.To obtain our results, we give new analysis of the behavior of random walks on a noisy clusterable graph, which consists of a large subset that induces a clusterable graph and a small unknown subgraph (the noise). We show that a random walk of appropriately chosen length from a typical vertex in a large cluster of the clusterable part will mix well in the corresponding cluster. Using this we are able to distinguish vertices from the clusterable part from those in the noisy part.

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Section: The Number Of Edges Changed Is At Mostmentioning

confidence: 99%

Robust Clustering Oracle and Local Reconstructor of Cluster Structure of Graphs

Pan¹

2020

Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms

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“…Assuming the links to be healthy, we have: (9) Equation (9) illustrates the consensus on (8). It can be verified that (10) With (11) The formation control equation can thus be written as (12) The main drawback of (12) is that it requires more communication as all the agents should know the…”

Section: Definitionmentioning

confidence: 99%

“…Once the estimate is available to reference or leader node formation can be maintained using (12). It is easy to visualize from (9) that the position of the agent in the formation depends on its own displacement and that of its one-hop neighbors.…”

Section: Estimation Based Formation Control Algorithmmentioning

confidence: 99%

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Robot Navigation and Guidance

Raol¹

2016

Mobile Intelligent Autonomous Systems

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“…Control loops integrated with communication channels for information exchange are called networked control systems (NCSs). A detailed review of NCSs can be found in ( [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] and the references therein). Researchers have investigated NCSs in the recent past owing to their advantages and applications [16][17][18][19][20][21][22][23][24][25].…”

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confidence: 99%

Formation Control in Multi-Agent Systems Over Packet Dropping Links

Srinivasan

Ayyagari²

2016

Mobile Intelligent Autonomous Systems

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Teams of autonomous agents working in coordination achieve greater efficiency and operation capability than agents performing solo-missions. Multi-agent systems have been investigated widely in the recent past owing to their wide applications and advantages. Formation control is one among the problems being investigated in both control and multi-agent systems paradigm. In formation control teams of agents moving together are required to maintain a pre-defined geometric configuration. Formation control problems have application in vehicle control, unmanned air-craft vehicles (UAVs), consensus and formation control of robots, in industrial robots to name a few. In order to maintain formation, agents in a team need to exchange information like relative displacement, velocity etc. These variables that are exchanged among agents in a team for maintaining formation are called coordination variables, and are used to achieve coordination among agents. Hence, there arises a need to transmit these coordination variables among all the agents in the network. One may visualize that any loss in coordination variable can jeopardize the formation. Communication channels are used for information exchange among agents and are the enabling factor of formation control algorithms.One major challenge in implementation of formation control problems stems from the packet loss that occur in these shared communication channel. In the presence of packet loss the coordination information among agents is lost. Moreover, there is a move to use wireless channels in formation control applications. It has been found in practice that packet losses are more pronounced in wireless channels, than their wired counterparts.In our analysis, we first show that packet loss may result in loss of rigidity. In turn this causes the entire formation to fail. Later, we present an estimation based formation control algorithm that is robust to packet loss among agents. The proposed estimation algorithm employs minimal spanning tree algorithm to compute the estimate of the node variables (coordination variables). Consequently, this reduces the communication overhead required for information exchange. Later, using simulation, we verify the data that is to be transmitted for optimal estimation of these variables in the event of a packet loss. Finally, the effectiveness of the proposed algorithm is illustrated using suitable simulation example.

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Hybrid Controllers for Systems with Random Communication Delays

Cited by 7 publications

References 22 publications

Robust Clustering Oracle and Local Reconstructor of Cluster Structure of Graphs

Robust Clustering Oracle and Local Reconstructor of Cluster Structure of Graphs

Robot Navigation and Guidance

Formation Control in Multi-Agent Systems Over Packet Dropping Links

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