Modeling orbital propagation using regression technique and artificial neural network

Orbital propagation models are used to predict the position and velocity of natural and artificial objects orbiting the Earth. It is crucial to get accurate predictions to ensure proper satellite operational planning and early detection of possible disasters. It became critical as the number of space objects grew due to many countries scrambling to explore space for various purposes such as communications, remote sensing, scientific mission, and many more. Physical-based and mathematical expression approaches provide orbital propagation with high accuracy. However, these approaches require substantial expenditure to provide suitable facilities and are complicated for those with no expertise in this field. The orbital propagation model is developed using regression techniques and artificial neural networks in this study. The aim is to have a reliable and precise orbital propagation model with minimal computational and cost savings. The past orbital data is used instead of complicated numerical equations and expensive tools. As a result, the trained orbital propagation model with accuracy up to 99.49% with a distance error of 18.73km per minute is achievable. The trained model can be improved further by modifying the network model and various input data. This model is also expected to provide vital information for organizations and anyone interested. Finally, this research can help organizations with insufficient resources to have their orbit propagation model without special tools or rely on other countries with satellite data at a lower cost

An adaptation of deep learning technique in orbit propagation model using long short-term memory

The orbit propagation model is used to predict the position and velocity of the satellites. It is crucial to obtain accurate predictions to ensure that satellite operation planning is in place and detects any possible disasters. However, the model's accuracy decreases as the propagation span increases if the input data are not updated. Therefore, to minimize these errors while still maintaining the model accuracy, a study is conducted. The Simplified General Perturbations-4 (SGP4) model and two-line elements (TLE) data are selected to perform this study. The problem is analyzed, and the deep learning technique is the proposed method to solve the issue. Next, the enhanced model is validated. The study aims to produce a reliable orbit propagation model and assist the satellite's operational planning. Also, the improved model can provide vital information for space-based organizations and anyone who may be affected

Machine Learning in Orbit Estimation: a Survey

Since the late '50s, when the first artificial satellite was launched, the number of resident space objects (RSOs) has steadily increased. It is estimated that around 1 Million objects larger than 1 cm are currently orbiting the Earth, with only 30,000, larger than 10 cm, presently being tracked. To avert a chain reaction of collisions, termed Kessler Syndrome, it is indispensable to accurately track and predict space debris and satellites' orbit alike. Current physics-based methods have errors in the order of kilometres for 7 days predictions, which is insufficient when considering space debris that have mostly less than 1 meter. Typically, this failure is due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, as well as specific unknown characteristics such as mass or geometry of the RSO. Leveraging data-driven techniques, namely machine learning, the orbit prediction accuracy can be enhanced: by deriving unmeasured objects' characteristics, improving non-conservative forces' effects, and by the superior abstraction capacity that Deep Learning models have of modelling highly complex non-linear systems. In this survey, we provide an overview of the current work being done in this field.Comment: submitted to AIAA Journal of Guidance, Control and Dynamic

Satellite Orbit Prediction Using Big Data and Soft Computing Techniques to Avoid Space Collisions

The number of satellites and debris in space is dangerously increasing through the years. For that reason, it is mandatory to design techniques to approach the position of a given object at a given time. In this paper, we present a system to do so based on a database of satellite positions according to their coordinates (x,y,z) for one month. We have paid special emphasis on the preliminary stage of data arrangement, since if we do not have consistent data, the results we will obtain will be useless, so the first stage of this work is a full study of the information gathered locating the missing gaps of data and covering them with a prediction. With that information, we are able to calculate an orbit error which will estimate the position of a satellite in time, even when the information is not accurate, by means of prediction of the satellite’s position. The comparison of two satellites over 26 days will serve to highlight the importance of the accuracy in the data, provoking in some cases an estimated error of 4% if the data are not well measured.Instituto de Investigación en Informátic

Nonlinear Robust Neural Control with Applications to Aerospace Vehicles

Nonlinear control has become increasingly more used over the last few decades, mainly due to the research and development of better analysis tools, that can simulate real-world problems, which are almost always, nonlinear. Nonlinear controllers have the advantage of being more accurate and efficient when dealing with complex scenarios, such as orbit control, satellite rendezvous, or attitude control, compared to linear ones. However, common nonlinear control techniques require having a high-fidelity model, which is often not the case, thereby limiting their use. Additionally, rapid advancements in the field of machine learning have raised the possibility of using tools like neural networks to learn the dynamics of nonlinear systems in an effort to compute control inputs without the need to solve the highly complex mathematical equations that some nonlinear controllers require to solve, in real-time, therefore bypassing the need of higher computational power, which can reduce costs and weight, in space missions. This dissertation will focus on the development of a neural controller based on H8 pseudolinear control, to be applied to the satellite attitude control problem, as well as the satellite orbit control problem. The resulting controller is proven to be robust when dealing with important disturbances that are relevant in space missions, due to being trained using H8 controller data. Moreover, since the original controller is pseudolinear, the neural controller can capture the nonlinearities that exist in the equations of motion as well as in the attitude dynamics equations.Nas últimas décadas, o controlo não-linear tem sido cada vez mais utilizado, maioritariamente devido ao desenvolvimento de melhores ferramentas de análise, utilizadas para a simulação problemas reais, que tendem a ser não-lineares. Os controladores não-lineares têm a vantagem de serem mais precisos e eficientes quando utilizados em situações complexas, como controlo orbital, rendezvous de satélites, e controlo de atitude, comparados com controladores lineares. No entanto, as técnicas comuns de controlo não-linear requerem o uso de modelos com alto grau de fidelidade, o que muitas vezes não é o caso, limitando assim a sua utilização. Além disso, os rápidos avanços no campo de machine learning levantaram a possibilidade de utilizar ferramentas como redes neuronais para aprender a dinâmica de sistemas não lineares, numa tentativa de poder computar as entradas de controlo sem a necessidade de resolver as equações matemáticas altamente complexas que alguns controladores não lineares necessitam que sejam resolvidas, em tempo real, contornando assim a necessidade de maior potência computacional, que pode reduzir custos e peso, em missões espaciais. Esta dissertação focar-se-á no desenvolvimento de um controlador neuronal, baseado em controlo pseudolinear por H8, com o intuito de ser aplicado no problema de controlo orbital, bem como no problema de controlo de atitude. O controlador resultante provou ser robusto ao lidar com perturbações importantes, relevantes em missões espaciais, devido ao facto de ter sido treinado usando dados do controlador H8. Além disso, como o controlador original é pseudolinear, o controlador neuronal pode captar as dinâmicas não lineares que existem nas equações de movimento, bem como nas equações da dinâmica de atitude

Space Debris as an international safety issue. Case studies in active removing techniques.

Η παρούσα διπλωματική εργασία πραγματοποιήθηκε υπό την αιγίδα του Τμήματος Πληροφορικής & Τηλεπικοινωνιών του Εθνικού και Καποδιστριακού Πανεπιστημίου Αθηνών για το Μεταπτυχιακό Πρόγραμμα Σπουδών «Διαστημικές Τεχνολογίες, Εφαρμογές και Υπηρεσίες». Στόχος της διατριβής ήταν να αναδείξει τη σημασία της έγκαιρης ανάληψης δράσης σε διεθνές επίπεδο, ώστε το ζήτημα των διαστημικών υπολειμμάτων να μην γίνει μείζονα απειλή κατά των επιχειρησιακών διαστημικών συστημάτων και των ανθρώπων που βρίσκονται σε τροχιά γύρω από τη γη. Παρόλο που το θέμα των διαστημικών υπολειμμάτων έχει απασχολήσει τον επιστημονικό, τεχνολογικό και πολιτικό κόσμο σχεδόν από την απαρχή της διαστημικής εποχής, δεν έχει ακόμα βρεθεί ουσιαστική λύση ούτε σε επιστημονικό, ούτε σε τεχνολογικό, ούτε σε πολιτικό επίπεδο. Στα παρακάτω κεφάλαια γίνεται μια ανάλυση του προβλήματος των διαστημικών υπολειμμάτων και αναφέρονται τα τεχνολογικά, νομικά και οικονομικά εμπόδια που παρουσιάζονται σε μια προσπάθεια απομάκρυνσης διαστημικών υπολειμμάτων. Στη συνέχεια αναπτύσσεται η έννοια της ασφάλειας στο διάστημα και πώς αυτή επηρεάζεται από την ύπαρξη διαστημικών υπολειμμάτων. Ταυτόχρονα γίνεται μια ανάλυση του ρίσκου που διέπει τις διαστημικές αποστολές, τόσο σε επίπεδο συστημάτων, όσο και σε επίπεδο ανθρώπινης ζωής σε συνάρτηση με την αύξηση των διαστημικών υπολειμμάτων. Από την ανάλυση αυτή δεικνύεται ότι η αύξηση των διαστημικών υπολειμμάτων λόγω περισσότερων διαστημικών αποστολών, καθώς και το ξεκίνημα της εποχής του διαστημικού τουρισμού, θα αποτελέσει έναν ισχυρό παράγοντα κινδύνου εάν δεν παρθούν άμεσα μέτρα. Στη συνέχεια παρουσιάζονται, σε τεχνικό επίπεδο, οι δυνατότητες εντοπισμού και παρατήρησης των διαστημικών υπολειμμάτων, καθώς και οι προοπτικές αυτών των συστημάτων. Επιπλέον, γίνεται αναφορά στο ποιες θα είναι οι μελλοντικές απαιτήσεις εντοπισμού και παρατήρησης των διαστημικών υπολειμμάτων ώστε να είναι αποτελεσματικές οι αποστολές απομάκρυνσης διαστημικών υπολειμμάτων. Συνεχίζοντας, παρουσιάζονται οι κύριες τεχνικές ενεργητικής απομάκρυνσης διαστημικών υπολειμμάτων, όπως αυτές μελετώνται και κατασκευάζονται από διαστημικούς οργανισμούς και διαστημικές εταιρείες. Τέλος, διενεργείται μια συγκριτική μελέτη των τεχνικών απομάκρυνσης διαστημικών υπολειμμάτων μέσω βαθμολόγησης τεσσάρων κύριων κριτηρίων και παρουσιάζεται ως αποτέλεσμα μια υπόθεση βέλτιστης τεχνολογίας απομάκρυνσης διαστημικών υπολειμμάτων. Από τη ανάλυση της Διπλωματικής Εργασίας γίνεται αντιληπτή η σημαντικότητα του να ληφθούν άμεσα αποφάσεις και να γίνουν οι κατάλληλες ενέργειες, ώστε τα διαστημικά υπολείμματα να μην αποτελέσουν κύριο παράγοντα κινδύνου για την ανθρωπότητα όπως τη γνωρίζουμε σήμερα.This thesis was conducted under the umbrella of the Department of Informatics & Telecommunication of the National and Kapodistrian University of Athens for the Postgraduate Program “Space Technologies, Applications and Services”. The aim of the thesis was to highlight the significance of taking timely action in an international level, for the space debris issue not to become a major threat against the operational space systems and the humans orbiting earth. Although the issue of space debris has occupied the scientific, technological and political world almost since the beginning of the space era, no substantial solution has yet been found either at a scientific, technological or political level. The following chapters provide an analysis of the space debris problem and present the technological, legal, and financial barriers to an effort to remove space debris. Then, the concept of security in space and the way it is affected by the existence of space debris is developed. At the same time, an analysis of the risk that governs space missions, both at the level of operation of space systems and at the level of human life in relation to the increase in space debris, is conducted. This analysis shows that the increase in space debris due to more space missions, as well as the onset of the era of space tourism, will be a strong risk factor if immediate measures are not taken. Then, at a technical level, the possibilities of locating and tracking space debris are presented, as well as the prospects of these technical systems. In addition, the future requirements for space debris detection and tracking, for space debris removal missions to be effective, are presented. Additionally, the main active space debris removal techniques studied and developed by space agencies and space companies are presented. Finally, a comparative study of space debris removal techniques is conducted by scoring four main criteria and a hypothesis of an optimal space debris removal technology is presented as a result. The analysis of the thesis shows the importance of making immediate decisions and taking the appropriate actions so that space debris does not constitute a major risk factor for humanity as we know it today