Consensus for agents with general dynamics using optimistic optimization

Buşoniu, Lucian; Morărescu, Irinel-Constantin

doi:10.1109/cdc.2013.6760956

Cited by 2 publications

(2 citation statements)

References 21 publications

(20 reference statements)

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“…?.iii) that shrink with the depth in the tree (??.ii). 1 Denote δ(d) = cγ d , the maximal diameter at depth d. DOO works by always partitioning further a set that is likely to contain the optimum of r. It does this by assigning upper bounds to all leaf sets U d,j , (d, j) ∈ L:…”

Section: Assumption 3 (Optimization)mentioning

confidence: 99%

“…The approach is also related to model-predictive control, which was applied to the consensus of linear agents in [12,6]. Our existing work includes [2], where we gave a different approach without analysis; and [1], which is a preliminary version of the present work. With respect to [1], here we introduce novel analysis that includes: using the deterministic OO algorithm, handling directed graphs, and a detailed investigation of the computational aspects; while the examples are also new.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consensus for black-box nonlinear agents using optimistic optimization

Buşoniu¹,

Morărescu²

2014

Automatica

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An important problem in multiagent systems is consensus, which requires the agents to agree on certain controlled variables of interest. We focus on the challenge of dealing in a generic way with nonlinear agent dynamics, represented as a black-box with unknown mathematical form. Our approach designs a reference behavior with a classical consensus method. The main novelty is using optimistic optimization (OO) to find controls that closely follow the reference behavior. The first advantage of OO is that it only needs to sample the black-box model of the agent, and so achieves our goal of handling unknown nonlinearities. Secondly, a tight relationship is guaranteed between computation invested and closeness to the reference behavior. Our main results exploit these properties to prove practical consensus. An analysis of representative examples builds additional insight and shows that in some nontrivial problems the optimization is easy to solve by OO. Simulations on these examples accompany the analysis.

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Section: Assumption 3 (Optimization)mentioning

confidence: 99%