Consensus Tracking of Multi-agent Systems in Presence of Uncertain Dynamics and Communications Faults

In this paper, the problem of consensus tracking uncertain multi-agent systems (MAS) with communication faults is addressed. The assumed to be undirected. A reinforced unscented Kalman filter (RUKF) employed adapt noise covariance matrices and estimate states MAS as well train neural network internal parameters by providing a set previous measurements. Chebyshev (CNN) incorporated learn plant. To prevent approximation errors hyperbolic tangent function based robust control term applied. Lyapunov approach guarantees stability RUKF which running in conjunction method. Numerical simulations are presented under different fault conditions show effectiveness proposed 5% less computation power compared adaptive UKF.