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Dynamic Average Consensus Over Strongly Connected Digraphs Based on Integral Surplus
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AI
DOI：10.1109/TAC.2025.3584699.png)
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Abstract
En 中文
This article addresses the design of a dynamic average consensus (DAC) algorithm over strongly connected but may not necessarily balanced digraphs, which seems to be the first time in the literature. Specifically, a new concept of integral surplus is proposed for DAC problems. On this basis, an integral surplus DAC algorithm is developed for agents to track the average of their multiple dynamic input signals with a bounded steady-state error. Such error is tunable by some algorithm parameters and even vanishes for special classes of input signals. Simulation examples are presented to verify the theoretical results.
Keywords:
Heuristic algorithms
Steady-state
Perturbation methods
Eigenvalues and eigenfunctions
Vectors
Urban areas
Training
Signal resolution
Protocols
Integral equations
Dynamic average consensus (DAC)
integral surplus
multiagent system
strongly connected digraph
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W