Dynamic max-consensus with local self-tuning

This work describes a novel control protocol for multi-agent systems to solve the dynamic max-consensus problem. In this problem, each agent has access an external time-varying scalar signal and objective estimate track maximum among all these signals by exploiting only local communications. The main strength of proposed is that it able self-tune its internal parameters in order achieve arbitrary small steady-state error without significantly affecting convergence time. We employ context distributed graph parameter estimations, such as size, diameter, radius, provide simulations scenario open systems.