This book explores the design of optimal trajectories for space maneuver vehicles (SMVs) using optimal control-based techniques. It begins with a comprehensive introduction to and overview of three main approaches to trajectory optimization, and subsequently focuses on the design of a novel hybrid optimization strategy that combines an initial guess generator with an improved gradient-based inner optimizer. Further, it highlights the development of multi-objective spacecraft trajectory optimization problems, with a particular focus on multi-objective transcription methods and multi-objective evolutionary algorithms. In its final sections, the book studies spacecraft flight scenarios with noise-perturbed dynamics and probabilistic constraints, and designs and validates new chance-constrained optimal control frameworks. The comprehensive and systematic treatment of practical issues in spacecraft trajectory optimization is one of the book’s major features, making it particularly suited for readers who are seeking practical solutions in spacecraft trajectory optimization. It offers a valuable asset for researchers, engineers, and graduate students in GNC systems, engineering optimization, applied optimal control theory, etc.

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 focuses on the design and application of advanced trajectory optimization and guidance and control (G&C) techniques for aerospace vehicles. Part I of the book focuses on the introduction of constrained aerospace vehicle trajectory optimization problems, with particular emphasis on the design of high-fidelity trajectory optimization methods, heuristic optimization-based strategies, and fast convexification-based algorithms. In Part II, various optimization theory/artificial intelligence (AI)-based methods are constructed and presented, including dynamic programming-based methods, model predictive control-based methods, and deep neural network-based algorithms. Key aspects of the application of these approaches, such as their main advantages and inherent challenges, are detailed and discussed. Some practical implementation considerations are then summarized, together with a number of future research topics. The comprehensive and systematic treatment of practical issues in aerospace trajectory optimization and guidance and control problems is one of the main features of the book, which is particularly suitable for readers interested in learning practical solutions in aerospace trajectory optimization and guidance and control. The book is useful to researchers, engineers, and graduate students in the fields of G&C systems, engineering optimization, applied optimal control theory, etc.

Improvement of spacecraft trajectory optimization approaches, methods, and techniques is critical for better mission design. Preliminary low-fidelity analysis precedes high-fidelity analysis to efficiently explore the space of a problem. The work of this dissertation extends an embedded boundary value problem (EBVP) technique for preliminary design in the two-body problem. The EBVP technique is designed for direct, unconstrained optimization using many, short-arc, embedded Lambert problems that discretize the trajectory. The short arcs share terminal positions to implicitly enforce position continuity and the instantaneous velocity discontinuities in between segments are the control. These coasting arcs and impulsive maneuvers in between segments are defined collectively as a coast-impulse model, similar to the well-known Sims-Flanagan model. Use of EBVPs is not new to spacecraft trajectory optimization, extensively used in primer vector theory, flyby-tour design, direct impulsive-maneuver optimization, and more. Lack of fast and accurate BVP solvers has prevented the use of the EBVP technique on problems with more than dozens of segments. For the two-body problem, a recently-developed Lambert solver, complete with the necessary partials, enables the extension of the EBVP technique to many hundreds to thousands of segments and hundreds of revolutions. The use of many short arcs guarantees existence and uniqueness for the Lambert problem of each segment. Furthermore, short arcs simultaneously approximate low thrust and eliminates the need to know the structure of a high-thrust impulsive-maneuver solution. A set of examples show the EBVP technique to be efficient, robust, and useful. In particular, an example using 256 revolutions, 6143 segments, and a constant flight time per segment, optimizes in 5.5 hours using a single processor. After this initial demonstration, the EBVP technique is improved by a function which enables variable flight time per segment. Guided by the well-known Sundman transformation, these piecewise Sundman transformation (PST) functions divide the total flight time of the trajectory into spatially-even arcs, importantly not modifying the dynamics. Flight-time functions and their dynamical regularization counterpart are shown to share similar behavior for Keplerian orbit propagation. The PST functions are also shown to extend the EBVP technique to a large design space, where a runtime-feasible transfer with 512 revs and 12287 segments is presented that significantly changes semimajor axis, eccentricity, and inclination. Moreover, another example is presented that transfers through the numerically challenging parabolic boundary, i.e. a transfer from a circular to hyperbolic orbit. Both these examples use an exponent of 3/2 for the PST to enforce the spatially-even arcs or equal steps in eccentric anomaly. Lastly, an optimal control problem is formulated to solve a class of many-revolution trajectories relevant to the EBVP technique. For transfers that minimize thrust-acceleration-squared, primer vector theory enables the mapping of direct, many-impulsive-maneuver trajectories to the indirect, continuous-thrust-acceleration equivalent. The mapping algorithm is independent of how the direct solution is obtained and the mapping computations only require a solver for a BVP and its partial derivatives. For the two-body problem, a Lambert solver is used. The mapping is simple because the impulsive maneuvers and co-states share the same linear space around an optimal trajectory. For numerical results, the direct coast-impulse solutions are demonstrated to converge to the indirect continuous solutions as the number of impulses and segments increase. The two-body design space is explored with a set of three many-revolution, many-segment examples changing semimajor axis, eccentricity, and inclination. The first two examples change either a small amount of semimajor axis or eccentricity, and the third example is a transfer to geosynchronous orbit. Using a single processor, the optimization runtime is seconds to minutes for revolution counts of 10 to 100, while on the order of one hour for examples with up to 500 revolutions. Any of these thrust-acceleration-squared solutions are good candidates to start a homotopy to a higher-fidelity minimization problem with practical constraints

This dissertation explores a novel method of solving low-thrust spacecraft targeting problems using reinforcement learning. A reinforcement learning algorithm based on Deep Deterministic Policy Gradients was developed to solve low-thrust trajectory optimization problems. The algorithm consists of two neural networks, an actor network and a critic network. The actor approximates a thrust magnitude given the current spacecraft state expressed as a set of orbital elements. The critic network evaluates the action taken by the actor based on the state and action taken. Three different types of trajectory problems were solved, a generalized orbit change maneuver, a semimajor axis change maneuver, and an inclination change maneuver. When training the algorithm in a simulated space environment, it was able to solve both the generalized orbit change and semimajor axis change maneuvers with no prior knowledge of the environment’s dynamics. The robustness of the algorithm was tested on an inclination change maneuver with a randomized set of initial states. After training, the algorithm was able to successfully generalize and solve new inclination changes that it has not seen before. This method has potential future applications in developing more complex low-thrust maneuvers or real-time autonomous spaceflight control.