Data-Driven iterative learning containment control for nonlinear MASs under sparse sensor attacks

doi:10.1016/j.cnsns.2026.109845

Data-Driven iterative learning containment control for nonlinear MASs under sparse sensor attacks

2026-02-22

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DOI：10.1016/j.cnsns.2026.109845

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• Considering the characteristics of information exchange between followers and multiple leaders, the distributed data-driven iterative learning containment con- trol scheme is presented, which can ensure that all followers converge into the convex hull formed by the leaders. • Based on redundant sensor channels, an adaptive switching strategy is designed to realize the ne management of transmission paths. The strategy can disconnect the attacked channel in time and automatically adjust to the normal working mode, which ensures reliable data conveyance. • Compared to existing solutions designed for speci c attack types [26], the con- structed dead-zone intrusion detection mechanism enables the detection of anoma- lous behaviors under multi-source hybrid attacks, which is achieved by utilizing the distributed containment error.

Keywords:

distributed containment control

iterative learning control

nonlinear multi-agent systems

sparse sensor attacks

adaptive switching strategy

Data-Driven iterative learning containment control for nonlinear MASs under sparse sensor attacks

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