A Neural Network Approach to Fault Detection in Spacecraft Attitude Determination and Control Systems

This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are dened such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time

Development and Initial On-orbit Performance of Multi-Functional Attitude Sensor using Image Recognition

This paper describes a multi-functional attitude sensor mounted on the “Innovative Satellite 1st” led by Japan Aerospace Exploration Agency which was launched in January 2019. In order to achieve the high accuracy determination in low cost, we developed a novel attitude sensor utilizing real-time image recognition technology, named “Deep Learning Attitude Sensor (DLAS)”. DLAS has two type of attitude sensors: Star Tracker(STT) and Earth Camera (ECAM). For the low-cost development, we adopted commercial off-the-shelf cameras. DLAS uses real-time image recognition technology and a new attitude determination algorithm. In this paper, we present the missions, methods and system configuration of DLAS and initial results of on-orbit experiment that was conducted after the middle of February 2019, and it is confirmed that attitude determinations using ECAM and STT are performed correctly

Autonomous artificial neural netwoek star tracker for spacecraft attitute determination

An artificial neural network based autonomous star tracker prototype for precise spacecraft attitude determination is developed. Night sky testing is used to validate a system consisting of a charged-coupled-device-based camera head unit and integrated control hardware and software. The artificial neural network star pattern match algorithm utilizes a sub catalog of the SKY2000 star catalog. The experimental results are real time comparisons of the star tracker observed motion with the rotational motion of the Earth. The results of a field-programmable-gate-array-based implementation of the star pattern match algorithm are also presented. A new technique of star pattern encoding that removes the star magnitude dependency is presented. The convex hull technique was developed in which the stars in the field of view are treated as a set of points. The convex hull of these points is found and stored as line segments and interior angles moving clockwise from the shortest segment. This technique does not depend on star magnitudes and allows a varying number of stars to be identified and used in calculating the attitude quaternion . This technique combined with feed-forward neural network pattern identification created a robust and fast technique for solving the "lost-in-space" problem. The time required to solve the "lost-in-space" problem for this star tracker prototype is on average 9.5 seconds. This is an improvement over the 60 seconds needed by the current off-the-shelf autonomous star tracker by Ball Aerospace, the CT-633. Initial acquisition after launch as well as recovery from a loss of attitude knowledge during the mission would occur significantly faster with this prototype system when compared to current commercially available autonomous star trackers

NASA Automated Rendezvous and Capture Review. Executive summary

In support of the Cargo Transfer Vehicle (CTV) Definition Studies in FY-92, the Advanced Program Development division of the Office of Space Flight at NASA Headquarters conducted an evaluation and review of the United States capabilities and state-of-the-art in Automated Rendezvous and Capture (AR&C). This review was held in Williamsburg, Virginia on 19-21 Nov. 1991 and included over 120 attendees from U.S. government organizations, industries, and universities. One hundred abstracts were submitted to the organizing committee for consideration. Forty-two were selected for presentation. The review was structured to include five technical sessions. Forty-two papers addressed topics in the five categories below: (1) hardware systems and components; (2) software systems; (3) integrated systems; (4) operations; and (5) supporting infrastructure

Static Attitude Determination Using Convolutional Neural Networks

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