Achieving Consensus Among Autonomous Dynamic Agents using Control Laws that Maintain Performance as Network Size Increases

Sponsor: National Science Foundation (NSF)

Award Number: 1740452

PI: Alexander Olshevsky

Abstract:

再保险

The main technical contribution will be to speed up a widely-used class of nearest neighbor interactions. It is common to optimize a global objective in multi-agent control by means of local updates that interleave the maximization local objectives with consensus terms that effectively couple these objectives. This project will develop techniques to speed up such consensus-like updates. By a judicious combination of weight-selection and extrapolation by each agent, the convergence time of consensus updates will be improved by one or several orders of magnitude. These speedups further imply quick convergence times for a number of multi-agent problems relying on consensus-like updates. The techniques applied mix recent advances from algebraic graph theory, optimization, switched dynamical systems, and the joint spectral radius.

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