  A novel distributed control law for consensus of networked double integrator
systems with biased measurements is developed in this article. The agents
measure relative positions over a time-varying, undirected graph with an
unknown and constant sensor bias corrupting the measurements. An adaptive
control law is derived using Lyapunov methods to estimate the individual sensor
biases accurately. The proposed algorithm ensures that position consensus is
achieved exponentially in addition to bias estimation. The results leverage
recent advances in collective initial excitation based results in adaptive
estimation. Conditions connecting bipartite graphs and collective initial
excitation are also developed. The algorithms are illustrated via simulation
studies on a network of double integrators with local communication and biased
measurements.
