Adaptive Iterative Learning Fault-Tolerant Consensus Control of Multiagent Systems Under Binary-Valued Communications

In this article, the iterative learning averaging consensus problem is studied for multiagent systems with system uncertainties, actuator faults, and binary-valued communications. Considering only binary-valued measurement information with stochastic noise can be received from its neighbors for each agent, a new two-iteration-scale framework that alternates estimation and control is designed. Under the proposed framework, each agent estimates the neighbors' states based on the empirical measurement method during a dwell iteration interval, during which each agent's states will keep constant along the iteration axis. Further, in view of the impacts of system uncertainties and actuator faults, a novel adaptive iterative learning fault-tolerant averaging consensus control scheme is designed based on its own states and the estimated neighbors' states. Finally, the resulting closed-loop system is rigorously proved to be stable, and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.