Nonlinear Adaptive Fault Estimation for Actuator Faults of Non-Gaussian Uncertain Systems with Sensor Measurement Error

This paper presents an actuator fault estimation method in non-Gaussian uncertain stochastic systems with sensor measurement error. Considering that multi-source nonlinearities of a class of stochastic systems cannot be approximated by a unified method, fuzzy logic is used to linearize nonlinear parameters, and the Lipschitz condition helps to proof the stability of the systems with nonlinear sensing functions. Moreover, to handle the sensor measurement error, an adaptive compensation scheme is devised. When measurement error occurs, the feedback contains the error information, and a feedback error composite function in the controller direct compensates the measurement error. When multiple actuator faults occur, an adaptive fusion observer simultaneously implements error masking and actuator fault estimation, Lyapunov functions prove the robust stability of the proposed fault estimation systems with measurement error, and simulation experiment verifies the performance.