Actuator Fault Diagnosis Of Nonlinear Systems Based On Unknown Input Root-Mean-Square Cubature Kalman Filter

This paper proposes an unknown input root mean square cubature Kalman filter algorithm, which is applied to the fault diagnosis of nonlinear systems with unknown input. Firstly, a standard linear regression equation with unknown input is constructed, and orthogonal trigonometric decomposition is combined to solve the equation to improve the estimation accuracy of unknown input. In addition, in order to improve the numerical stability of algorithm, the root mean square algorithm is introduced into the error covariance matrix calculated from the unknown input estimation and state estimation results. Secondly, the root mean square value of the sliding window of residual obtained from the difference between the measured value and the estimated value is computed to judge whether the actuator has a fault. The generalized regression neural network is used for fault identification. Finally, a single link manipulator system is taken for simulation verification.