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Adaptive distributed dynamic average consensus with prescribed performance
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AI
DOI：10.1002/asjc.3534.png)
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Abstract
En 中文
A novel adaptive distributed estimation algorithm is developed for dynamic average consensus problem. The algorithm enables a group of agents to accurately track the average value of signals that are available locally to each agent. Unlike most of existing approaches that rely on error transformation techniques, our algorithm employs an adaptive-gain based barrier function to simultaneously meet prescribed performance specifications of transient and steady state. It is both simple and easy to implement and is not dependent on bounded reference signals and their derivatives. Additionally, it can operate under less strict assumptions than those that assume absolute bounds on the signals. Simulation results are presented to demonstrate the effectiveness of the developed algorithm.
Keywords:
adaptive distributed estimation
dynamic average consensus
prescribed performance
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