Improved Adaptive Estimation for Intermittent Time-Varying Faults in Non-Gaussian Stochastic Systems

Abstract

This paper studies an improved adaptive fault estimation for the non-Gaussian stochastic systems with intermittent time-varying actuator faults. Type II fuzzy theory approximates the nonlinear dynamical systems with non-Gaussian stochastic outputs, disturbance and singular matrices under the harsh environments. An estimation observer with fuzzy and bionic adaptive laws is proposed to accurately estimate the different amplitudes of actuator faults. Where the bionic variable parameter algorithm that mimics animal predation behavior improves the sensitivity of observer to the incipient intermittent faults. Lyapunov function and linear matrix inequality prove the robust stability, and simulation results illustrate the effectiveness of the methods.