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This thesis concerns the use of genetic algorithms in the optimization of the trajectories of low thrust spacecraft. Genetic algorithms are programming tools which use the principles of biological evolution and adaptation to optimize processes. These algorithms have been found to be very useful in many different engineering disciplines. The goal of this project is to determine their applicability to the generation and optimization of low thrust spacecraft trajectories. This thesis describes the basic operating principles of genetic algorithms and then applies them to two different missions.
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 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.
A class of methods for the numerical solution of optimal control problems is analyzed and applied to the optimization of finite-thrust spacecraft trajectories. These methods use discrete approximations to the state and control histories, and a discretization of the equations of motion to derive a mathematical programming problem which approximates the optimal control problem, and which is solved numerically. This conversion is referred to as "transcription." Transcription methods were developed in the sixties and seventies; however, for nonlinear problems, they were used in conjunction with a "state-elimination" procedure which propagated the integration forward over the trajectory, and resulted in a relatively unconstrained programming problem which was easier to handle. Recent advances in nonlinear programming, however, have made it feasible to solve the original heavily-constrained nonlinear programming problem, which is referred to as the "direct transcription" of the optimal control problem. This method is referred to as "direct transcription and nonlinear programming." A recently developed method for solving optimal trajectory problems uses a piecewise-polynomial representation of the state and control variables and enforces the equations of motion via a collocation procedure, resulting in a nonlinear programming problem, which is solved numerically. This method is identified as being of the general class of direct transcription methods described above. Also, a new direct transcription method which discretizes the equations of motion using a parallel-shooting approach is developed. For both methods, the relationship between the original optimal control problem and the approximating nonlinear programming problem is investigated by comparing the optimal control necessary conditions with the optimality conditions for the discretized problem. Both methods are applied to thrust-limited spacecraft trajectory problems, including finite-thrust transfer, rendezvous, and orbit insertion, a low-thrust escape, and a low-thrust Earth-moon transfer. The basic methods have been modified to accurately model discontinuities in the optimal control, to provide efficient handling of those portions of the trajectory which can be determined analytically, i.e., coast arcs of the two-body problem, and to allow the simultaneous use of several coordinate systems.
The book focuses on symplectic pseudospectral methods for nonlinear optimal control problems and their applications. Both the fundamental principles and engineering practice are addressed. Symplectic pseudospectral methods for nonlinear optimal control problems with complicated factors (i.e., inequality constraints, state-delay, unspecific terminal time, etc.) are solved under the framework of indirect methods. The methods developed here offer a high degree of computational efficiency and accuracy when compared with popular direct pseudospectral methods. The methods are applied to solve optimal control problems arising in various engineering fields, particularly in path planning problems for autonomous vehicles. Given its scope, the book will benefit researchers, engineers and graduate students in the fields of automatic control, path planning, ordinary differential equations, etc.
The direct approach for trajectory optimization was found to be very robust. For most problems, solutions were obtained even with poor initial guesses for the controls. The direct approach was also found to be only slightly less accurate than the indirect methods found in the literature (within 0.7%). The present study investigates minimum-time and minimum-fuel low-thrust trajectory problems via a single shooting direct method. Various Earth-based and interplanetary case studies have been examined and have yielded good agreement with similar cases in the literature. Furthermore, new near-optimal trajectory problems have been successfully solved. A multiple-orbit thrust parameterization strategy was also developed to solve near-optimal very-low-thrust Earth-based transfers. Lastly, this thesis examines the use of the high-accuracy complex-step derivative approximation method for solving low-thrust transfer problems. For certain very nonlinear transfer problems, the complex-step derivative approximation was found to increase the robustness of the single shooting direct method.
"An algorithm based on the mechanics of natural genetics is used to solve two different optimization problems. The algorithm combines survival of the fittest among string structures with randomized information exchange to form a search algorithm. Initiated with a population of bits-coded individuals, the genetic algorithm searches for the optimal solutions generation-by-generation. The three main operations in the genetic algorithm: Reproduction, Crossover, and Mutation give this algorithm the power of searching. In the first part of this thesis, the genetic algorithm is used to determine the optimal trajectories of the aeroassisted vehicle reentry problem. The trajectories to be optimized are determined not only by the parameter searching but also under a height constraint which make this study more interesting. In the second part of this thesis the genetic algorithm is used for designing three-axis bang-bang controllers for the time-optimal rigid spacecraft reorientation problem. The firing times of the bang-bang controller and the time of rotation are determined by searching for the angular velocity thresholds and the reorientation time"--Abstract, leaf iv.
All-electric satellites are gaining favor among the manufacturers and operators of satellites in Geostationary Earth Orbit (GEO) due to cost saving potential. These satellites have the capability of performing all propulsive tasks with electric propulsion including transfer to GEO. Although fuel-efficient, electric thrusters lead to long transfer, during which the health and the usability of spacecraft is affected due to its exposure to hazardous space radiation in the Van Allen belts. Hence, determining electric orbit-raising trajectory that minimize transfer time is crucial for all-electric satellite operation. This thesis proposes a novel method to determine minimum-time orbit-raising trajectory by blending the ideas of direct optimization and guidance-like trajectory optimization schemes. The proposed methodology is applicable for both planar and non-planar transfers and for transfers starting from arbitrary circular and elliptic orbits. Therefore, it can be used for rapidly analyzing various orbit-raising mission scenarios. The methodology utilizes the variational equations of motion of the satellite in the context of the two-body problem by considering the low-thrust of an electric engine as a perturbing force. The no-thrust condition due to Earth's shadow is also considered. The proposed methodology breaks the overall optimization problem into multiple sub-problems and each sub-problem minimizes a desired objective over the sun-lit part of the trajectory. Two different objective types are considered. Type I transfers minimize the deviation of the total energy and eccentricity of final position from the GEO, while type II transfers minimize the deviation of total energy and angular momentum. Using the developed tool, several mission scenarios are analyzed including, a new type of mission scenarios, in which more than one thruster type are used for the transfer. The thesis presents the result for all studied scenarios and compares the performance of Type I and Type II transfers.