Lyapunov-Based Control of a Nonlinear Multi-Agent System with a Time-Varying Input Delay under False-Data-Injection Attacks

This paper introduces a Lyapunov-based nonlinear control scheme that mitigates False-Data-Injection (FDI) attacks in real-time for a centralized multi-agent system (MAS) with nonlinear Euler-Lagrange dynamics with additive disturbances and input delays. Since the state tracking error is faulty during FDI attacks, the central controller cannot solely rely on the state tracking error to coordinate the MAS. Therefore, a novel feedback signal is designed to address this challenge and to ensure that the MAS achieves the control objective. The proposed controller combines both learning and model-based approaches to estimate the agents' states and to detect and respond to FDI attacks. A neural network (NN) is used to detect the FDI attacks, where the update laws for the NN weights are designed based on the corresponding stability analysis. Lyapunov-Krasovskii (LK) functionals are used in the Lyapunov-based stability analysis to ensure semi-global uniformly ultimately bounded tracking. A simulation of multi-agent robots with nonlinear Euler-Lagrange dynamics is provided, demonstrating the promising performance of the developed method to respond to FDI attacks.