UC Santa Cruz

Motivated by the design of distributed observers with good performance and robustness to measurement and communication noise, the problem of obtaining a global estimate, over a graph, of a common process is considered. We propose a global consensus algorithm that satisfies a pre-specified rate of convergence and has optimized robustness to both communication and measurement noise. The convergence rate and the robustness to communication and measurement noise of the proposed consensus algorithm are characterized in terms of (nonlinear and linear) optimization problems. The stability and robustness properties of the proposed algorithm are shown analytically and validated numerically.