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A Novel Adaptive Dynamic Average Consensus Algorithm with Application to DC Microgrids
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DOI：10.1109/JAS.2025.125387.png)
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Abstract
En 中文
The dynamic average consensus (DAC) algorithm is to enable a group of networked agents to track the average of their time-varying reference signals. For most existing DAC algorithms, a necessary assumption is that the upper bounds of the reference signals and their derivatives are known in advance, thereby posing significant challenges in practical scenarios. Introducing adaptive gains in DAC algorithms provides a remedy by relaxing this assumption. However, the current adaptive gains used in this type of DAC algorithms are non-decreasing and may increase to infinity if persist disturbance exists. In order to overcome this defect, this paper presents a novel DAC algorithm with modified adaptive gains. This approach obviates the necessity for prior knowledge concerning the upper bounds of the reference signals and their derivatives. Moreover, the adaptive gains are able to remain bounded even in the presence of external disturbances. Furthermore, the proposed adaptive DAC algorithm is employed to address the distributed secondary control problem of DC microgrids. Comparative case studies are provided to verify the superiority of the proposed DAC algorithm.
Keywords:
Adaptive gain
DC microgrids
dynamic average consensus
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116
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