Security-Driven Adaptive Iterative Learning Formation Control for Multiagent Systems

Zhiqiang Li

doi:10.1109/tsmc.2025.3622120

Security-Driven Adaptive Iterative Learning Formation Control for Multiagent Systems

2025-01-01

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DOI：10.1109/tsmc.2025.3622120

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This article addresses the adaptive iterative learning formation control (AILFC) problem of multiagent systems (MASs) with unmeasurable state subject to Denial-of-Service (DoS) attacks. To alleviate DoS attacks, a neural network (NN)-based compensation mechanism is proposed to learn communication signals, and a learning-based distributed output observer is designed to estimate the leader output. Moreover, an improved extended state observer (ESO) is designed to deal with unmeasurable states and total disturbance. Then, a time-varying boundary layer method with a normalized function is constructed to address the initial error problem. Furthermore, a multiple observer-based AILFC scheme is developed via the backstepping control technique, and the stability analysis of MASs is given by the Lyapunov theory. Finally, a simulation example is shown to illustrate the effectiveness of the developed AILFC algorithm.

Security-Driven Adaptive Iterative Learning Formation Control for Multiagent Systems

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