Fuzzy modeling-based fault diagnosis and tolerant control for complex non-Gaussian stochastic systems

This paper discusses a novel fault diagnosis and tolerant control problem for a class of non-Gaussian stochastic distribution systems with unknown fault based on two-step fuzzy modeling. Following square fuzzy logic approximation for the output probability density functions (PDFs) of non-Gaussian processes, the T-S weight models are employed to describe the nonlinear relations between the fuzzy weight dynamics and the control input. By utilizing the typical projection algorithm, the adaptive fuzzy filter is designed to successfully estimate the size of system fault. Meanwhile, the error system stability in present of fault can also be guaranteed. Moreover, the feedback control input can be constructed by using convex optimization algorithm. The satisfactory control performance and stability can be achieved by the designed optimization algorithm.