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Data-Driven Iterative Learning Consensus Tracking Based on Robust Neural Models for Unknown Heterogeneous Nonlinear Multiagent Systems With Input Constraints
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作者 Chong Zhang Yunfeng Hu +2 位作者 TingTing Wang Xun Gong Hong Chen 《IEEE/CAA Journal of Automatica Sinica》 2025年第10期2153-2155,共3页
Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol ... Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT). 展开更多
关键词 dynamic linearization data model dldm consensus tracking problem input constraints consensus tracking unknown heterogeneous nonlinear multiagent systems robust neural models data driven iterative learning zeroing neural networks znns
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