Privacy-Preserving Model-Free Adaptive Sliding Mode Control for MASs under Dynamic Sparse Attacks

This paper investigates the data-driven consensus control problem for a class of discrete-time single-input single-output (SISO) nonlinear multi-agent systems (MASs) subject to limited bandwidth resources, dynamic sparse attacks, and communication disturbances. Consensus errors are transmitted over multiple channels, where an attacker can compromise a time-varying subset of these channels. First, to alleviate the communication burden while enhancing resilience, a distributed dynamic event-triggered mechanism (DDETM) is proposed to reduce unnecessary data transmission. Second, an encryption-decryption scheme using multiple symmetric keys is adopted to protect data confidentiality. In conjunction, a data-fusion algorithm is designed to accurately estimate the true output from the corrupted and noisy encrypted data received at triggering instants. Subsequently, a fully distributed model-free adaptive sliding mode control (FDMFASMC) algorithm is developed to ensure the robustness and convergence of the MASs. Finally, a numerical simulation is presented to validate the effectiveness and performance of the proposed control strategy.