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

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