Okarma, Krzysztof - ed. ; Skrzypczyński, Piotr - ed. ; Stefanowski, Jerzy - ed.
For multi-agent systems with missing iteration batches and randomly varying lengths, a data-driven adaptive iterativa learning control strategy based on neural networks is proposed. Considering the situation where the iteration batches of the system vary randomly with the number of iterations, first, an iterative batch missing fault model that follows a random probability distribution is designed. ; Secondly, based on the radial basis function neural network method, a novel datadriven adaptive iterative learning consistency control algorithm is proposed. The stability analysis of the system is strictly performed using the Lyapunov function, and the sufficient conditions for consistency of multi-agent systems are derived through relevant mathematical derivations. ; This enables multi-agent systems to achieve state consistency even when there are missing faults in the iterative batches. Finally, the feasibility of the algorithm is verified through numerical simulations in a multi-agent system.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 36, number 3 (2026)
Biblioteka Uniwersytetu Zielonogórskiego
Nov 6, 2026
Nov 6, 2026
0
https://zbc.uz.zgora.pl/repozytorium/publication/115178
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