Design of Robust State Estimation Filter in The Presence of Uncertainty, Fault, and Unknown Input

In this study, state estimation problem is considered for a class of uncertain time varying nonlinear stochastic system. The considered system is subject to faults, disturbance, uncertainty and nonlinearity. For this purpose, a robust extended Kalman filter is proposed. So, we design robust extended Kalman filter and the upper bound of state estimation error covariance is yield for all stochastic uncertainties. The performance of the filter is demonstrated on the state estimation for a nonlinear helicopter unmanned aerial vehicle (HUAV). The results of the proposed filter are simulated and it is compared with conventional nonlinear robust filter (NRF) and EKF. Result shows the better efficiency of the proposed method.