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Appointed-Time Dynamic Average Consensus: A Holistic Planning and Multi-Step Control Framework
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PRE
AI
DOI：10.1109/TCSII.2023.3243997.png)
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摘要
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Most existing solutions to the dynamic average consensus (DAC) problem assume access to the full dynamic of reference signals or their derivatives. Such constraints are more stringent and impose restrictions on use. Hence, we revisit the DAC problem in this brief and present its solution by developing a holistic planning and multi-step control (HPMSC) framework. Here, the HPMSC allows the agents to achieve DAC in an appointed settling time without involving any other parameters, which is called appointed-time dynamic average consensus (AT-DAC). Under the HPMSC framework, a sampling-data-based AT-DAC controller is designed, which guarantees that agents can track the average value of dynamic reference signals at a pre-appointed time. Compared with the existing results on DAC, a nice highlight of this brief is the implementation of zero-error DAC with weaker conditions imposed on the reference signals, without the need to know the upper bounds on their derivatives and without additional common function conditions. Finally, the performance validation of theoretical results is demonstrated by a numerical simulation.
Keyword:
Appointed convergence time
dynamic average consensus
HPMSC framework
sampling-data transmission
期刊
I
IF:
4.9
论文数:
8.8K
被引数:
2.5W
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