Abstract
In this letter, the average consensus problem has been considered for undirected networks under finite bit-rate communication. While other algorithms reach approximate average consensus or require global information about the network for reaching the exact average consensus, we propose a fully distributed consensus algorithm that incorporates an adaptive quantization scheme and achieves convergence to the exact average while only requiring knowledge of an upper bound of the network diameter. Using Lyapunov stability analysis, we characterize the convergence properties of the resulting nonlinear quantized system. Moreover, we provide a fully distributed strategy to escape plateaux, i.e., situations where the Lyapunov function stops descending. Simulation results justify the performance of our proposed algorithm.
Evagoras Makridis
PhD Student | Distributed Decision and Control of Networked Systems
My research interests include autonomous systems in networks, distributed optimization, and data-driven sequential decision-making (Reinforcement Learning), with applications in quadrotor navigation, and resource management.