Reinforced unscented Kalman filter for consensus achievement of uncertain multi-agent systems subject to actuator faults

In this paper, actuator fault detection and reconstruction in consensus tracking of uncertain multi-agent systems (MAS) is addressed. The communication is assumed to be connected undirected. An adaptive fault detection method is developed to detect actuator faults. A novel-reinforced unscented Kalman filter (RUKF) is employed to reconstruct the faults by adjusting the noise covariance matrices of unscented Kalman filter (UKF) as well as to train neural network internal parameters by providing a set of previous measurements. A Chebyshev neural network (CNN) is incorporated to learn the uncertain plant. To prevent the neural network approximation errors a hyperbolic tangent function-based robust control term is applied. The Lyapunov stability approach guarantees the stability of the proposed RUKF, which runs in conjunction with robust control method. Lastly, numerical simulations are presented to show the effectiveness of the proposed RUKF under actuator abrupt, intermittent, and transient fault conditions.