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Encoding-Decoding-Based Model-Free Adaptive Control of Nonlinear Multi-Agent Systems Under False Data Injection Attacks
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DOI：10.1109/tce.2026.3720435.png)
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Abstract
En 中文
The communication security of multi-agent systems (MASs) with interconnected consumer electronic devices is a critical issue in open network environments. This paper investigates the encoding-decoding-based model-free adaptive (MFA) security tracking control problem for nonlinear MASs under false data injection (FDI) attacks. Due to the shared nature of information transmission among agents, a sophisticated attacker can disrupt communication reliability by injecting false data into both intra- and inter-agent communication channels. To improve communication efficiency and channel reliability, a uniform quantization-based encoding-decoding scheme is developed. Then, an MFA controller is designed by utilizing the decoding data to improve the control performance in the presence of FDI attacks. Subsequently, using a dynamic linearization technique and an attack compensation strategy, an observer is developed to minimize the negative effects caused by FDI attacks, quantization errors, and parameter estimation errors. This method provides a communication-efficient and secure control framework for clusters of networked consumer electronic devices under FDI attacks. Sufficient criteria are established for MASs ensuring the boundedness of the tracking error. Finally, two simulation examples demonstrate the validity of the proposed encoding-decoding-based MFA tracking control method.
Keywords:
Encoding-decoding mechnaism
false data injection attacks
model-free adaptive control
nonlinear multi-agent systems
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10.9
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5.3K
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