Model-Free Adaptive Iterative Learning Sliding Mode Control for Nonlinear Multi-Agent Systems Under DoS Attacks

This article investigates the issue of distributed model-free adaptive iterative learning sliding mode control (DMFAILSMC) for unknown nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. Firstly, considering the measurement channel is affected by DoS attacks, the compensation algorithm based on historical iteration data is devised to minimize the adverse effects. Secondly, the compact-form dynamic linearization (CFDL) technique is used to transform the compensated system into the equivalent data model along the iterative axis. Subsequently, by using one-step-ahead synchronization measurement error, the distributed sliding mode controller is developed to achieve consistent tracking of all agents. Finally, two examples have validated the effectiveness of the proposed scheme.