Differentially Private Model-Free Adaptive Consensus Control for Nonlinear Multiagent Systems Under FDI Attacks

To address the security and privacy challenges in Internet of Things (IoT) enabled systems, this paper introduces a differential privacy-based distributed model-free adaptive control framework for secure consensus control of nonlinear IoT-based multi-agent systems (MASs). First, by embedding a differential privacy mechanism into inter-agent communications, the privacy protection accuracy is theoretically guaranteed while ensuring data confidentiality. Second, a distributed model-free adaptive control algorithm is developed, which only uses local input-output (I/O) data, thereby removing the need for explicit system models and global network topology information, offering high scalability and robustness for large-scale IoT networks with unknown dynamics. Furthermore, an integrated control framework is proposed to achieve consensus control for heterogeneous nonlinear MASs under simultaneous FDI attacks and privacy perturbations. It is theoretically established that the method maintains mean-square boundedness for estimation errors, consensus errors, and output trajectories despite the joint impact of adversarial attacks and privacy noise. Simulation results demonstrate that the approach effectively resists stealthy data tampering attacks while maintaining communication privacy and system stability.