Download Free Design Of Deep Q Networks For Transfer Time Prediction Of Spacecraft Orbit Raising Book in PDF and EPUB Free Download. You can read online Design Of Deep Q Networks For Transfer Time Prediction Of Spacecraft Orbit Raising and write the review.

Recently, there has been a surge in use of electric propulsion to transfer satellites to the geostationary Earth orbit (GEO). Traditionally, the transfer times to reach GEO using all electric propulsion are obtained by solving challenging trajectory optimization problems, whose solution rely on numerical schemes that are not only computationally intensive, but also lack automated implementation capabilities. This naturally creates a hindrance to their incorporation within Deep Reinforcement Learning (DRL) framework, which combines Reinforcement Learning (RL) and Deep Learning to solve trajectory planning problems in near real-time. The operation of DRL, as typically used in trajectory planning, relies on a Q-value. In the electric orbit-raising problem under consideration in this thesis, this Q-Value requires computation of transfer time in near real-time to have practical DRL training times. In our work, this Q-value is predicted by a set of deep neural networks (DNNs) instead of solving traditional optimization problems. This thesis aims at designing a set of DNNs that can serve as a Q-value (transfer time) predictor for different orbit-raising mission scenarios. To this end, we investigate different architectures for DNNs to determine the optimal DNN configuration that can predict the transfer time for each of the mission scenarios. Experimental results indicate that our designed DNNs can predict the transfer time for different scenarios with an accuracy of over 99%. We also compare the results from our designed DNNs with the contemporary Machine Learning (ML) algorithms, such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT) for regression. Experimental results indicate that our best-performing DNNs can provide an improvement in mean error of transfer time prediction of up to 14.05x for non-planar transfers and up to 254x for planar transfers.
The use of electric propulsion (EP) in satellites for transfer to geosynchronous equatorial orbit (GEO) is increasingly gaining importance among the space industry all around the world, and is proven a key for new space missions. In a conventional launch, the satellite is placed into a geostationary transfer orbit (GTO) by the launch vehicle and uses chemical propellants to reach GEO. This orbital transfer maneuver typically takes a few days. However, even though EP is far more e cient than the conventional chemical propulsion, its low thrust generation adds the complexity of longer transfer time from an equatorial orbit to GEO. This longer transit time leads to exposure of spacecraft to hazardous radiation of Van Allen belts. Therefore, there is a need to develop a method to determine the minimum transfer time trajectory for all-electric low thrust orbit raising problem. This thesis proposes a new formulation that facilitates the application of reinforcement learning to the problem of orbit raising. This work is based on the approach that the electric orbit-raising problem is posed as a sequence of multiple trajectory optimization sub-problems. Each sub-problem aims to move the spacecraft closest to GEO by minimizing a convex combination of suitably selected objectives. A mathematical formulation for the orbit-raising problem is proposed in the framework of reinforcement learning to enable adaptive modi cation of the objective function weights during a transfer. Due to high dimensionality of the planning states of the orbit-raising problem, arti cial neural networks are then constructed and trained on orbit-raising scenarios in order to compute the reward functions associated with reinforcement learning. The reward function for a planning state is de ned as the time required to reach GEO from that planning state. With the help of numerical simulations for planar and non-planar transfer scenarios, it is demonstrated that there is a reduction in transfer time for low-thrust orbit raising problem with the proposed methodology.
This is a long-overdue volume dedicated to space trajectory optimization. Interest in the subject has grown, as space missions of increasing levels of sophistication, complexity, and scientific return - hardly imaginable in the 1960s - have been designed and flown. Although the basic tools of optimization theory remain an accepted canon, there has been a revolution in the manner in which they are applied and in the development of numerical optimization. This volume purposely includes a variety of both analytical and numerical approaches to trajectory optimization. The choice of authors has been guided by the editor's intention to assemble the most expert and active researchers in the various specialities presented. The authors were given considerable freedom to choose their subjects, and although this may yield a somewhat eclectic volume, it also yields chapters written with palpable enthusiasm and relevance to contemporary problems.
This book describes the basic concepts of spacecraft operations for both manned and unmanned missions. The first part of the book provides a brief overview of the space segment. The next four parts deal with the classic areas of space flight operations: mission operations, communications and infrastructure, the flight dynamics system, and the mission planning system. This is followed by a part describing the operational tasks of the various subsystems of a classical satellite in Earth orbit. The last part describes the special requirements of other mission types due to the presence of astronauts, the approach of a satellite to another target satellite, or leaving Earth orbit in interplanetary missions and landing on other planets and moons. The 2nd edition is published seven years after the first edition. It contains four new chapters on flight procedures, the human factors, ground station operation, and software and systems. In addition, several chapters have been extensively expanded. The entire book has been brought up to date and the language has been revised. This book is based on the “Spacecraft Operations Course” held at the German Space Operations Center. However, the target audience of this book is not only the participants of the course, but also students of technical and scientific courses, as well as technically interested people who want to gain a deeper understanding of spacecraft operations.
Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms
Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.
The challenge of communication in planetary exploration has been unusual. The guidance and control of spacecraft depend on reliable communication. Scientific data returned to earth are irreplaceable, or replaceable only at the cost of another mission. In deep space, communications propagation is good, relative to terrestrial communications, and there is an opportunity to press toward the mathematical limit of microwave communication. Yet the limits must be approached warily, with reliability as well as channel capacity in mind. Further, the effects of small changes in the earth's atmosphere and the interplanetary plasma have small but important effects on propagation time and hence on the measurement of distance. Advances are almost incredible. Communication capability measured in 18 bits per second at a given range rose by a factor of 10 in the 19 years from Explorer I of 1958 to Voyager of 1977. This improvement was attained through ingenious design based on the sort of penetrating analysis set forth in this book by engineers who took part in a highly detailed and amazingly successful pro gram. Careful observation and analysis have told us much about limitations on the accurate measurement of distance. It is not easy to get busy people to tell others clearly and in detail how they have solved important problems. Joseph H. Yuen and the other contribu tors to this book are to be commended for the time and care they have devoted to explicating one vital aspect of a great adventure of mankind.
The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
Based on years of research conducted at the NASA Jet Propulsion Laboratory, Low-Energy Lunar Trajectory Design provides high-level information to mission managers and detailed information to mission designers about low-energy transfers between Earth and the moon. The book answers high-level questions about the availability and performance of such transfers in any given month and year. Low-energy lunar transfers are compared with various other types of transfers, and placed within the context of historical missions. Using this book, designers may reconstruct any transfer described therein, as well as design similar transfers with particular design parameters. An Appendix, “Locating the Lagrange Points,” and a useful list of terms and constants completes this technical reference. Surveys thousands of possible trajectories that may be used to transfer spacecraft between Earth and the moon, including transfers to lunar libration orbits, low lunar orbits, and the lunar surface Provides information about the methods, models, and tools used to design low-energy lunar transfers Includes discussion about the variations of these transfers from one month to the next, and the important operational aspects of implementing a low-energy lunar transfer Additional discussions address navigation, station-keeping, and spacecraft systems issues