Robust Dynamic Average Consensus for a Network of Agents with Time-varying Reference Signals

This paper presents a continuous dynamic average consensus (DAC) algorithm for a group of agents to estimate the average of their time-varying reference signals cooperatively. We propose a consensus algorithm that is robust to agents joining and leaving the network, at the same time, avoid the chattering phenomena and guarantee zero steady-state consensus error. Our algorithm is an edge-based protocol with smooth functions in its internal structure to avoid the chattering effect. Furthermore, each agent can only perform local computations and can only communicate with its local neighbors. For a balanced and strongly connected underlying communication graph, we provide the convergence analysis to determine the consensus design parameters that guarantee the estimate of the average to asymptotically converge to the average of the time-varying reference signals. We provide simulation results to validate the proposed consensus algorithm and perform a performance comparison of the proposed algorithm to existing algorithms in the literature.