Mixed fault diagnosis scheme for satellite formation

The purpose of this paper is to present a solution for the uncertain fault with the propulsion subsystem of satellite formation, using the Lur’e differential inclusion linear state observers (DILSOs) and fuzzy wavelet neural network (FWNN) to perform fault detection and diagnosis.

The uncertain fault system cannot be described based on the accurate differential equations. The set-value mapping is introduced into the state equations to solve the problem of uncertainty, but it will cause output uncertainty. The problem can be solved by linearization of Lur’e differential inclusion state observers. The Lur’e DILSOs can be used to detect uncertain fault. The fault isolation and estimation can be performed using the FWNN.

The mixed approach from fault detection and diagnosis has featured fast and correct to found the uncertain fault. The simulation results to indicate that the methods of design are not only effective but also have the advantages of good approximation effect, fast detection speed, relatively simple structure and prior knowledge and realization of adaptive learning.

The hybrid algorithm can be extensively applied to engineering practice and find uncertain faults of the propulsion subsystem of satellite formation promptly.

This paper provides a fast, effective and simple mixed fault detection and diagnosis scheme for satellite formation.