  This paper presents a consensus algorithm for a multi-agent system where each
agent has access to its imperfect own state and neighboring state measurements.
The measurements are subject to deterministic disturbances and the proposed
algorithm provides a minimum-energy estimate of the measured states which is
instrumental in achieving consensus by the nodes. It is shown that the proposed
consensus algorithm converges exponentially in the absence of disturbances, and
its performance under bounded continuous disturbances is investigated as well.
The convergence performance of the proposed method is further studied using
simulations where we show that consensus is achieved despite using large
measurement errors.
