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• W2985278936 abstract "This paper is mainly devoted to a distributed iterative learning control design for a class of nonlinear discrete-time multi-agent systems in the presence of randomly varying iteration lengths. A stochastic variable is introduced and utilized to construct a consensus error with iteration-varying lengths. The distributed ILC law using the consensus error term is considered, contraction mapping and $lambda $ -norm technique methods are employed to develop a sufficient condition for the asymptotic stability of ILC. It is shown that all agents can be guaranteed to achieve finite-time tracking with randomly varying iteration lengths, even under the condition that the desired trajectory is available to not all, but only a portion of agents. The proposed algorithm is also extended to achieve consensus control for switching topologies multi-agent systems with iteration-varying lengths. Two illustrative examples are given to demonstrate the effectiveness of the theoretical results." @default.
• W2985278936 created "2019-11-22" @default.
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• W2985278936 date "2019-01-01" @default.
• W2985278936 modified "2023-10-17" @default.
• W2985278936 title "Iterative Learning Consensus Tracking Control for Nonlinear Multi-Agent Systems With Randomly Varying Iteration Lengths" @default.
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• W2985278936 doi "https://doi.org/10.1109/access.2019.2950428" @default.
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