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Iterative learning consensus control for one-sided Lipschitz multi-agent systems
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DOI：10.1002/rnc.6943.png)
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Abstract
En 中文
By applying iterative learning control approach, the consensus is studied for multi-agent systems (MASs) with one-sided Lipschitz (OSL) nonlinearity. Firstly, the P-type and D-type learning schemes with initial state learning are introduced for such MASs. Then, utilizing the OSL and the quadratically inner-bounded constraints, the convergence conditions of the consensus algorithms are presented and analyzed under a directed communication graph. We show that both algorithms, on a fixed finite-time interval, can achieve perfect consensus tracking. Finally, the correctness of the obtained results is illustrated with simulation examples.
Keywords:
consensus
iterative learning control
multi-agent systems
one-sided Lipschitz
quadratically inner-bounded
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