Sensor fault-tolerant attitude determination system based on the nonlinear interacting-multiple-model approach

Abstract

This paper presents a methodology for satellite sensors' fault detection and diagnosis (FDD) in a fault-tolerant attitude determination system using a nonlinear interacting-multiple-model approach (IMM). Moreover, the fault-tolerant attitude determination system utilizes the IMM approach in the presence of sensors' total fault to estimate faulty sensors and attitude states. This methodology relies on the most conventional satellite attitude sensors including magnetometer, sun sensor, and gyroscope measurements. The presented scheme leads to a significant computational time-saving method due to considering a pre-filter Kalman for fault detection, hence, the proposed FDD approach consists of two stages. In the first stage, the pre-filter Kalman detects any sensor fault and attitude states. In contrast, in the next stage, fault diagnosis and optimal attitude estimation are obtained through the IMM approach. Therefore, the IMM algorithm will be activated to extract the optimal estimation and identify faulty sensor(s). A Monte Carlo type approach is used to verify this technique for various initial conditions. Through numerical and Monte Carlo simulations, it is shown that the proposed scheme is robust against sensors' total fault, and the system is capable of detecting and isolating accurately the faulty sensor. Hardware in the loop tests using satellite dynamics simulator are also used to validate the presented system performance.