Data-Driven Adaptive Iterative Learning Control for Consensus Tracking of Multiagent Systems Subject to Hybrid Cyber Attacks.

Abstract

This work investigates the data-driven iterative learning consensus control problem for nonlinear multiagent systems (MASs) subject to hybrid cyberattacks, including denial-of-service (DoS) attacks and deception attacks acting on different communication channels. Considering connectivity-maintained hybrid attacks, a data-driven adaptive iterative learning control (ILC) approach is developed for consensus control, where system uncertainties and cyberattacks are compensated through adaptive iterative estimation schemes. Furthermore, the proposed approach is extended to the case with connectivity-paralyzed hybrid attacks, under which the influence of attack occurrence on the achievable control performance is explicitly characterized. A rigorous convergence analysis is provided, and numerical examples are presented to demonstrate the effectiveness of the proposed approach.