Distributed Robust Fault Estimation Using Relative Measurements for Leader-Follower Multiagent Systems

In this article, the problem of distributed robust fault estimation (FE) for leader-follower multiagent systems using relative measurements is considered. A distributed intermediate-based fault estimator is constructed using the local relative measurements and the state estimation from neighbors. The gain matrices of the fault estimator are calculated based on H-infinity performance in terms of linear matrix inequality (LMI) to improve the robustness of the estimator. Then, the LMI is separated and simplified by spectral decomposition, and its equivalent condition is proposed based on the maximum and minimum eigenvalue. A distributed eigenvalue estimation algorithm based on the power method is presented to fully distribute the proposed FE scheme. Finally, the numerical simulations are provided to verify the effectiveness of the proposed scheme.

Automated summary

This article discusses the problem of distributed robust fault estimation for leader-follower multiagent systems using relative measurements. A distributed intermediate-based fault estimator is constructed, and the gain matrices are calculated based on H-infinity performance to improve robustness. A distributed eigenvalue estimation algorithm based on the power method is proposed to fully distribute the fault estimation scheme.