Adaptive consensus estimation of multi-agent systems

This paper considers the effects of a penalty term in both the state and parameter estimates for multi-agent systems. It is assumed that the plant parameters are desired to be identified on-line and N agents are available to implement adaptive observers. Using an additive term which takes the form of a penalty on the mismatch between the state and parameter estimates, the proposed adaptive consensus estimation scheme ensures that both state and parameter estimates reach consensus. While the proposed adaptive consensus identifiers assume an all-to-all connectivity, the abstract framework that the adaptive identifiers are examined under, allows for any form of agent connectivity to be examined. As a measure of agreement of the estimates that is independent of the network topology, the deviation from the mean estimate for both the state and parameter estimates is defined and shown to converge to zero. Simulation studies of a second order system provide a verification of the proposed theoretical predictions.