The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter-dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.

Solutions to trajectory optimization problems are carried out by the direct and indirect methods. Under broad heading of these methods, numerous algorithms such as collocation, direct, indirect and multiple shooting methods have been developed and reported in the literature. Each of these algorithms has certain advantages and limitations. For example, direct shooting technique is not suitable when the number of nonlinear programming variables is large. Indirect shooting method requires analytical derivatives of the control and co-states function and a poorly guessed initial condition can result in numerical unstable values of the adjoint variable. Multiple shooting techniques can alleviate some of these difficulties by breaking down the trajectory into several segments that help in reducing the non-linearity effects of early control on later parts of the trajectory. However, multiple shooting methods then have to handle more number of variables and constraints to satisfy the defects at the segment joints. The sie of the nonlinear programming problem in the collocation method is also large and proper locations of grid points are necessary to satisfy all the path constraints. Stochastic methods such as Genetic algorithms, on the other hand, also require large number of function evaluations before convergence. To overcome some of the limitations of the conventional methods, improved solution techniques are developed. Three improved methods are proposed for the solution of trajectory optimization problems. They are • a genetic algorithm employing dominance and diploidy concept. • a collocation method using chebyshev polynomials , and • a hybrid method that combines collocation and direct shooting technique A conventional binary-coded genetic algorithm uses a haploid chromosome, where a single string contains all the variable information in the coded from. A diploid, as the name suggests, uses pair of chromosomes to store the same characteristic feature. The diploid genetic algorithm uses a dominant map for decoding genotype into a stable, consistent phenotype. In dominance, one allele takes precedence over another. Diploidy and dominance helps in retaining the previous best solution discovered and shields them from harmful selection in a changing environment. Hence, diploid and dominance affect a king of long-term memory in the genetic algorithm. They allow alternate solutions to co-exist. One solution is expressed and the other is held in abeyance. In the improved diploid genetic algorithm, dominant and recessive genes are defined based on the fitness evaluation of each string. The genotype of fittest string is declared as the dominant map. The dominant map is dynamic in nature as it is replaced with a better individual in future generations. The concept of diploidy and dominance in the improved method mimics closer to the principles used in human genetics as compared to any such algorithms reported in the literature. It is observed that the improved diploid genetic algorithm is able to locate the optima for a given trajectory optimization problem with 10% lower computational time as compared to the haploid genetic algorithm. A parameter optimization problem arising from an optimal control problem where states and control are approximated by piecewise Chebyshev polynomials is well known. These polynomials are more accurate than the interpolating segments involving equal spaced data. In the collocation method involving Chebyshev polynomials, derivatives of two neighboring polynomials are matched with the dynamics at the nodal points. This leads to a large number of equality constraints in the optimization problem. In the improved method, derivative of the polynomial is also matched with the dynamics at the center of segments. Though is appears the problem size is merely increased, the additional computations improve the accuracy of the polynomial for a larger segment. The implicit integration step size is enhanced and overall size of the problem is brought down to one-fourth of the problem size defined with a conventional collocation method using Chebyshev polynomials. Hybrid method uses both collocation and direct shooting techniques. Advantages of both the methods are combined to give more synergy. Collocation method is used in the starting phase of the hybrid method. The disadvantage of standalone collocation method is that tuning of grid points is required to satisfy the path constraints. Nevertheless, collocation method does give a good guess required for the terminal phase of the hybrid method, which uses a direct shooting approach. Results show nearly 30% reduction in computation time for the hybrid approach as compared to a method in which direct shooting alone is used, for the same initial guess of control. The solutions obtained from the three improved methods are compared with an indirect method. The indirect method requires derivations of the control and adjoint equations, which are difficult and problem specific. Due to sensitivity of the costate variables, it is often difficult to find a solution through the indirect method. Nevertheless, these methods do provide an accurate result, which defines a benchmark for comparing the solutions obtained through the improved methods. Trajectory design and optimization of a RLV(Reusable Launch Vehicle) Demonstrator mission is considered as a test problem for evaluating the performance of the improved methods. The optimization problem is difficult than a conventional launch vehicle trajectory optimization problem because of the following two reasons. • aerodynamic lift forces in the RLV add one more dimension to the already complex launch vehicle optimization problem. • as RLV performs a sub orbital flight, the ascent phase trajectory influences the re-entry trajectory. Both the ascent and re-entry optimization problem of the RLV mission is addressed. It is observed that the hybrid method gives accurate results with least computational effort, as compared with other improved techniques for the trajectory optimization problem of RLV during its ascent flight. Hybrid method is then successfully used during the re-entry phase and in designing the feasible optimal trajectories under the dispersion conditions. Analytical solutions obtained from literature are used to compare the optimized trajectory during the re-entry phase. Trajectory optimization studies are also carried out for the off-nominal performances. Being a thrusting phase, the ascent trajectory is subjected to significant deviations, mainly arising out of solid booster performance dispersions. The performance index during rhe ascent phase is modified in a novel way for handling dispersions. It minimizes the state errors in a least square sense, defined at the burnout conditions ensure possibilities of safe re-entry trajectories. The optimal trajectories under dispersion conditions serve as a benchmark for validating the closed-loop guidance algorithm that is developed for the ascent phase flight. Finally, an on-line trajectory command-reshaping algorithm is developed which meets the flight objectives under the dispersion conditions. The guidance algorithm uses a pre-computed trajectory database along with some real-time measured parameters in generating the optimal steering profiles. The flight objectives are met under the dispersion conditions and the guidance generated steering profiles matches closely with the optimal trajectories.

<p>The 9:2 lunar synodic resonant near rectilinear halo orbit (NRHO) is the chosen orbit for the Gateway, a future lunar space station constructed by the National Aeronautics and Space Administration (NASA) as well as several commercial and international partners. Designing trajectories in this sensitive lunar region combined with the absence of a singular systematic methodology to approach mission design poses challenges as researchers attempt to design transfers to and from this nearly stable orbit. This investigation builds on previous research in Poincar\'e mapping strategies to design transfers from the 9:2 NRHO using higher-dimensional maps and maps with non-state variables. First, Poincar\'e maps are applied to planar transfers to demonstrate the utility of hyperplanes and establish that maps with only two or three dimensions are required in the planar problem. However, with the addition of two state variables, the spatial problem presents challenges in visualizing the full state. Higher-dimensional maps utilizing glyphs and color are employed for spatial transfer design involving the 9:2 NRHO. The visualization of all required dimensions on one plot accurately reveals low cost transfers into both a 3:2 planar resonant orbit and an L2 vertical orbit. Next, the application of higher-dimensional maps is extended beyond state variables. Visualizing time-of-flight on a map axis enables the selection of faster transfers. Additionally, glyphs and color depicting angular momentum rather than velocity lead to transfers with nearly tangential maneuvers. Theoretical minimum maneuvers occur at tangential intersections, so these transfers are low cost. Finally, a map displaying several initial and final orbit options, discerned through the inclusion of Jacobi constant on an axis, eliminates the need to recompute a map for each initial and final orbit pair. Thus, computation time is greatly reduced in addition to visualizing more of the design space in one plot. The higher-dimensional mapping strategies investigated are relevant for transfer design or other applications requiring the visualization of several dimensions simultaneously. Overall, this investigation outlines Poincar\'e mapping strategies for transfer scenarios of different design space dimensions and represents initial research into non-state variable mapping methods.</p>

<p>Secondary payloads have a rich and successful history of utilizing cheap rides to orbit to perform outstanding missions in Earth orbit, and more recently, in cislunar space and beyond. New launch vehicles, namely the Space Launch System (SLS), are increasing the science opportunity for rideshare class missions by providing regular service to the lunar vicinity. However, trajectory design in a multi-body regime brings a host of novel challenges, further exacerbated by constraints generated from the primary payload’s mission. Often, secondary payloads do not possess the fuel required to directly insert into lunar orbit and must instead perform a lunar flyby, traverse the Earth-Moon-Sun system, and later return to the lunar vicinity. This investigation develops a novel framework to construct low-cost, end-to-end lunar transfer trajectories for secondary payload missions. The proposed threephase approach provides unique insights into potential lunar transfer geometries. The phases consist of an arc from launch to initial perilune, an exterior transfer arc, and a lunar approach arc. The space of feasible transfers within each phase is determined through low-dimension grid searches and informed filtering techniques, while the problem of recombining the phases through differential corrections is kept tractable by reducing the dimensionality at each phase transition boundary. A sample mission demonstrates the trajectory design approach and example solutions are generated and discussed. Finally, alternate strategies are developed to both augment the analysis and for scenarios where the proposed three-phase technique does not deliver adequate solutions. The trajectory design methods described in this document are applicable to many upcoming secondary payload missions headed to lunar orbit, including spacecraft with only low-thrust, only high-thrust, or a combination of both. </p>

<p dir="ltr">As traffic in cislunar space grows and missions become increasingly complex, effective understanding of relative motion between spacecraft is paramount. Additional tools are necessary to more accurately determine favorable relative behaviors in a multi-body gravitational environment. In this investigation, relative states are characterized as linear combinations of principal stretching and restoring directions to introduce techniques for relative trajectory design in the circular restricted three-body problem. These dynamically-informed directions form sets of orthonormal vector bases employed to describe and design spacecraft motion relative to a reference trajectory. Properties unique to 3x3 subsets of the state transition matrix are exploited to derive methodologies for two relative motion applications. First, the formulation is presented from a spacecraft loitering perspective, providing a framework to methodically determine ballistic relative trajectory options for visiting spacecraft. Secondly, modifications are introduced for flexible maneuver planning in a collision avoidance scenario. The two methodologies are assessed for a variety of test cases along four different periodic orbits in the Earth-Moon system.</p>

<div>There is an increasing interest in future space missions devoted to the exploration of key moons in the Solar system. These many different missions may involve libration point orbits as well as trajectories that satisfy different endgames in the vicinities of the moons. To this end, an efficient design strategy to produce low-energy transfers between the vicinities of adjacent moons of a planetary system is introduced that leverages the dynamics in these multi-body systems. Such a design strategy is denoted as the moon-to-moon analytical transfer (MMAT) method. It consists of a general methodology for transfer design between the vicinities of the moons in any given system within the context of the circular restricted three-body problem, useful regardless of the orbital planes in which the moons reside. A simplified model enables analytical constraints to efficiently determine the feasibility of a transfer between two different moons moving in the vicinity of a common planet. Subsequently, the strategy builds moon-to-moon transfers based on invariant manifold and transit orbits exploiting some analytical techniques. The strategy is applicable for direct as well as indirect transfers that satisfy the analytical constraints. The transition of the transfers into higher-fidelity ephemeris models confirms the validity of the MMAT method as a fast tool to provide possible transfer options between two consecutive moons. </div><div> </div><div>The current work includes sample applications of transfers between different orbits and planetary systems. The method is efficient and identifies optimal solutions. However, for certain orbital geometries, the direct transfer cannot be constructed because the invariant manifolds do not intersect (due to their mutual inclination, distance, and/or orbital phase). To overcome this difficulty, specific strategies are proposed that introduce intermediate Keplerian arcs and additional impulsive maneuvers to bridge the gaps between trajectories that connect any two moons. The updated techniques are based on the same analytical methods as the original MMAT concept. Therefore, they preserve the optimality of the previous methodology. The basic strategy and the significant additions are demonstrated through a number of applications for transfer scenarios of different types in the Galilean, Uranian, Saturnian and Martian systems. Results are compared with the traditional Lambert arcs. The propellant and time-performance for the transfers are also illustrated and discussed. As far as the exploration of Phobos and Deimos is concerned, a specific design framework that generates transfer trajectories between the Martian moons while leveraging resonant orbits is also introduced. Mars-Deimos resonant orbits that offer repeated flybys of Deimos and arrive at Mars-Phobos libration point orbits are investigated, and a nominal mission scenario with transfer trajectories connecting the two is presented. The MMAT method is used to select the appropriate resonant orbits, and the associated impulsive transfer costs are analyzed. The trajectory concepts are also validated in a higher-fidelity ephemeris model.</div><div> </div><div>Finally, an efficient and general design strategy for transfers between planetary moons that fulfill specific requirements is also included. In particular, the strategy leverages Finite-Time Lyapunov Exponent (FTLE) maps within the context of the MMAT scheme. Incorporating these two techniques enables direct transfers between moons that offer a wide variety of trajectory patterns and endgames designed in the circular restricted three-body problem, such as temporary captures, transits, takeoffs and landings. The technique is applicable to several mission scenarios. Additionally, an efficient strategy that aids in the design of tour missions that involve impulsive transfers between three moons located in their true orbital planes is also included. The result is a computationally efficient technique that allows three-moon tours designed within the context of the circular restricted three-body problem. The method is demonstrated for a Ganymede->Europa->Io tour.</div>

<p> </p> <p>In the coming decades, numerous missions plan to exploit multi-body orbits for operations. Given the complex nature of multi-body systems, trajectory designers must possess effective tools that leverage aspects of the dynamical environment to streamline the design process and enable these missions. In this investigation, a particular class of dynamical structures, quasi-periodic orbits, are examined. This work summarizes a computational framework to construct quasi-periodic orbits and a design framework to leverage quasi-periodic motion within the path planning process. First, quasi-periodic orbit computation in the Circular Restricted Three-Body Problem (CR3BP) and the Bicircular Restricted Four-Body Problem (BCR4BP) is summarized. The CR3BP and BCR4BP serve as preliminary models to capture fundamental motion that is leveraged for end-to-end designs. Additionally, the relationship between the Earth-Moon CR3BP and the BCR4BP is explored to provide insight into the effect of solar acceleration on multi-body structures in the lunar vicinity. Characterization of families of quasi-periodic orbits in the CR3BP and BCR4BP is also summarized. Families of quasi-periodic orbits prove to be particularly insightful in the BCR4BP, where periodic orbits only exist as isolated solutions. Computation of three-dimensional quasi-periodic tori is also summarized to demonstrate the extensibility of the computational framework to higher-dimensional quasi-periodic orbits. Lastly, a design framework to incorporate quasi-periodic orbits into the trajectory design process is demonstrated through a series of applications. First, several applications were examined for transfer design in the vicinity of the Moon. The first application leverages a single quasi-periodic trajectory arc as an initial guess to transfer between two periodic orbits. Next, several quasi-periodic arcs are leveraged to construct transfer between a planar periodic orbit and a spatial periodic orbit. Lastly, transfers between two quasi-periodic orbits are demonstrated by leveraging heteroclinic connections between orbits at the same energy. These transfer applications are all constructed in the CR3BP and validated in a higher-fidelity ephemeris model to ensure the geometry persists. Applications to ballistic lunar transfers are also constructed by leveraging quasi-periodic motion in the BCR4BP. Stable manifold trajectories of four-body quasi-periodic orbits supply an initial guess to generate families of ballistic lunar transfers to a single quasi-periodic orbit. Poincare mapping techniques are used to isolate transfer solutions that possess a low time of flight or an outbound lunar flyby. Additionally, impulsive maneuvers are introduced to expand the solution space. This strategy is extended to additional orbits in a single family to demonstrate "corridors" of transfers exist to reach a type of destination motion. To ensure these transfers exist in a higher fidelity model, several solutions are transitioned to a Sun-Earth-Moon ephemeris model using a differential corrections process to show that the geometries persist.</p>

Integrating unmanned aerial vehicles (UAVs) as aerial user equipments (UEs) into cellular networks is now considered as a promising solution to provide extensive wireless connectivity for supporting UAV-centric commercial or civilian applications. However, the co-existence of UAVs with conventional terrestrial UEs is one of the primary challenges for this solution. Flying at higher altitudes with maneuverability advantage, UAVs are able to establish line-of-sight (LoS) connectivity with more base stations (BSs) than terrestrial UEs. Although LoS connectivity reduces the communication delay of UAVs, they also simultaneously increase the interference that UAVs cause to terrestrial UEs. In scenarios involving multiple UAVs, LoS connectivity can even lead to interference issues among themselves. In addition, LoS connectivity leads to extensive overlapping coverage areas of multiple BSs for UAVs, forcing them to perform frequent handovers during the flight if the received signal strength (RSS)-based handover policy is employed. The trajectories and BS associations of UAVs, along with their radio resource allocation are essential design parameters aimed at enabling their seamless integration into cellular networks, with a particular focus on managing interference levels they generate and reducing the redundant handovers they performe. Hence, this thesis designs two joint trajectory and handover management approaches for single-UAV and multi-UAVs scenarios, respectively, aiming to minimize the weighted sum of three key performance indicators (KPIs): transmission delay, up-link interference, and handover numbers. The approaches are based on deep reinforcement learning (DRL) frameworks with dueling double deep Q-network (D3QN) and Q-learning with a MIXer network (QMIX) algorithms being selected as the training agents, respectively. The choice of these DRL algorithms is motivated by their capability in designing sequential decision-making policies consisting of trajectory design and handover management. Results show that the proposed approaches effectively address the aforementioned challenges while ensuring the low transmission delay of cellular-connected UAVs. These results are in contrast to the performance of benchmark scheme, which directs UAVs to follow the shortest path and perform handovers based on RSS. Specifically, when considering the single-UAV scenario, the D3QN-based approach reduces the up-link interference by 18% and the handover numbers by 90% with a 59% increase in transmission delay as compared to the benchmark. The equivalent delay increase is 15 microseconds, which is considered negligible. For the multi-UAVs scenario, the QMIX-based approach jointly optimizes three performance metrics as compared to the benchmark scheme, resulting in a 70% decrease in interference, a 91% decrease in handover numbers, and a 47% reduction in transmission delay. It is noteworthy that an increase of UAVs operating within the same network leads to performance degradation due to UAVs competing for communication resources and mutual interference. When transitioning from the single-UAV scenario to the multi-UAVs scenario, the performance of the benchmark scheme experiences a significant decline, with an increase of 199% in interference, 89% in handover numbers, and 652% in transmission delay. In contrast, the proposed QMIX algorithm effectively coordinates multiple UAVs, mitigating performance degradation and achieving performance similar to the D3QN algorithm applying in the single-UAV scenario: an interference increase of 9%, a handover numbers increase of 9% and a delay increase of 152%. The delay increase is attributed to the reduced communication resources available to each individual UAVs, given the constant communication resources of the network. / Att integrera obemannade flygfordon (UAV) som flyganvändarutrustning (UE) i cellulära nätverk anses nu vara en lovande lösning för att tillhandahålla omfattande trådlös anslutning för att stödja UAV-centrerade kommersiella eller civila tillämpningar. Men samexistensen av UAV med konventionella markbundna UE är en av de främsta utmaningarna för denna lösning. Flygande på högre höjder med manövrerbarhetsfördelar kan UAV:er etablera siktlinje (LoS)-anslutning med fler basstationer (BS) än markbundna UE. Även om LoS-anslutning minskar kommunikationsfördröjningen för UAV:er, ökar de samtidigt störningen som UAV:er orsakar för markbundna UE. I scenarier som involverar flera UAV:er kan LoS-anslutning till och med leda till störningsproblem sinsemellan. Dessutom leder LoS-anslutning till omfattande överlappande täckningsområden för flera BS:er för UAV, vilket tvingar dem att utföra frekventa överlämningar under flygningen om den mottagna signalstyrkan (RSS)-baserad överlämningspolicy används. UAV:s banor och BS-associationer, tillsammans med deras radioresursallokering, är väsentliga designparametrar som syftar till att möjliggöra deras sömlösa integrering i cellulära nätverk, med särskilt fokus på att hantera störningsnivåer de genererar och minska de redundanta handovers de utför. Därför designar denna avhandling två gemensamma bana och handover-hanteringsmetoder för en-UAV-respektive multi-UAV-scenarier, som syftar till att minimera den viktade summan av tre nyckelprestandaindikatorer (KPI:er): överföringsfördröjning, upplänksinterferens och överlämningsnummer . Tillvägagångssätten är baserade på ramverk för djup förstärkning inlärning (DRL) med duellerande dubbla djupa Q-nätverk (D3QN) och Q-lärande med ett MIXer-nätverk (QMIX) algoritmer som väljs som träningsagenter. Valet av dessa DRL-algoritmer motiveras av deras förmåga att utforma sekventiella beslutsfattande policyer som består av banadesign och handover-hantering. Resultaten visar att de föreslagna tillvägagångssätten effektivt tar itu med ovannämnda utmaningar samtidigt som de säkerställer den låga överföringsfördröjningen för mobilanslutna UAV:er. Dessa resultat står i kontrast till prestanda för benchmark-schemat, som styr UAV:er att följa den kortaste vägen och utföra överlämningar baserat på RSS. Närmare bestämt, när man överväger singel-UAV-scenariot, minskar det D3QN tillvägagångssättet upplänksinterferensen med 18% och överlämningssiffrorna med 90% med en 59% ökning av överföringsfördröjningen jämfört med riktmärket. Den ekvivalenta fördröjningsökningen är 15 mikrosekunder, vilket anses vara försumbart. För scenariot med flera UAV:er optimerar det QMIX-baserade tillvägagångssättet tillsammans tre prestandamått jämfört med benchmark-schemat, vilket resulterar i en 70% minskning av störningar, en 91% minskning av överlämningssiffror och en 47% minskning av överföringsfördröjningen. Det är anmärkningsvärt att en ökning av UAV:er som arbetar inom samma nätverk leder till prestandaförsämring på grund av UAV:er som konkurrerar om kommunikationsresurser och ömsesidig störning. Vid övergången från scenariot med en UAV till scenariot med flera UAV, upplever prestanda för benchmark-schemat en betydande nedgång, med en ökning på 199% av störningar, 89% i överlämnandetal och 652% i överföringsfördröjning. Däremot koordinerar den föreslagna QMIX-algoritmen effektivt flera UAV, vilket minskar prestandaförsämring och uppnår prestanda liknande D3QN-algoritmen som tillämpas i single-UAV-scenariot: en störningsökning på 9%, en ökning av antalet överlämningar med 9% och en fördröjningsökning på 152%. Ökningen av fördröjningen tillskrivs de minskade kommunikationsresurserna tillgängliga för varje enskild UAV, givet nätverkets konstanta kommunikationsresurser.

<p dir="ltr">The recent and upcoming increase in spaceflight missions to the lunar vicinity necessitates methodologies to enable operations beyond the Earth. In particular, there is a pressing need for a Space Domain Awareness (SDA) and Space Situational Awareness (SSA) architecture that encompasses the realm of space beyond the sub-geosynchronous region to sustain humanity's long-term presence in that region. Naturally, the large distances in the cislunar domain restrict access rapid and economical access from the Earth. In addition, due to the long ranges and inconsistent visibility, the volume contained within the orbit of the Moon is inadequately observed from Earth-based instruments. As such, space-based assets to supplement ground-based infrastructure are required. The need for space-based assets to support a sustained presence is further complicated by the challenging dynamics that manifest in cislunar space. Multi-body dynamical models are necessary to sufficiently model and predict the motion of any objects that operate in the space between the Earth and the Moon. The current work seeks to address these challenges in dynamical modeling and cislunar accessibility via the exploration of resonant orbits. These types of orbits, that are commensurate with the lunar sidereal period, are constructed in the Earth-Moon Circular Restricted Three-Body Problem (CR3BP) and validated in the Higher-Fidelity Ephemeris Model (HFEM). The expansive geometries and energy options supplied by the orbits are favorable for achieving recurring access between the Earth and the lunar vicinity. Sample orbits in prograde resonance are explored to accommodate circumlunar access from underlying cislunar orbit structures via Poincaré mapping techniques. Orbits in retrograde resonance, due to their operational stability, are employed in the design of space-based observer constellations that naturally maintain their relative configuration over successive revolutions. </p><p dir="ltr"> Sidereal resonant orbits that are additionally commensurate with the lunar synodic period are identified. Such orbits, along with possessing geometries inherent to sidereal resonant behavior, exhibit periodic alignments with respect to the Sun in the Earth-Moon rotating frame. This characteristic renders the orbits suitable for hosting space-based sensors that, in addition to naturally avoiding eclipses, maintain visual custody of targets in the cislunar domain. For orbits that are not eclipse-favorable, a penumbra-avoidance path constraint is implemented to compute baseline trajectories that avoid Earth and Moon eclipse events. Constellations of observers in both sidereal and sidereal-synodic resonant orbits are designed for cislunar SSA applications. Sample trajectories are assessed for the visibility of various targets in the cislunar volume, and connectivity relative to zones of interest in Earth-Moon plane. The sample constellations and observer trajectories demonstrate the utility of resonant orbits for various applications to sustain operations in cislunar space. </p>

Preliminary interplanetary trajectory designs frequently use simplified two-body orbital mechanics and linked conics methodology to model the complex trajectories in multi-body systems. Incorporating gravity assists provides highly efficient interplanetary trajectories, enabling otherwise infeasible spacecraft missions. Future missions may employ powered gravity assists, using a propulsive maneuver during the flyby, improving the overall trajectory performance. This dissertation provides a complete description and analysis of a new interplanetary trajectory design tool known as TRACT (TRAjectory Configuration Tool). TRACT is capable of modeling complex interplanetary trajectories, including multiple ballistic and/or powered gravity assists, deep space maneuvers, parking orbits, and other common maneuvers. TRACT utilizes an adaptable architecture of modular boundary value problem (BVP) algorithms for all trajectory segments. A bi-level optimization scheme is employed to reduce the number of optimization variables, simplifying the user provided trajectory information. The standardized optimization parameter set allows for easy use of TRACT with a variety of optimization algorithms and mission constraints. The dissertation also details new research in powered gravity assists. A review of literature on optimal powered gravity assists is presented, where many optimal solutions found are infeasible for realistic spacecraft missions. The need was identified for a mission feasible optimal powered gravity assist algorithm using only a single impulsive maneuver. The solution space was analyzed and a complete characterization was developed for solution types of the optimal single-impulse powered gravity assist. Using newfound solution space characteristics, an efficient and reliable optimal single-impulse powered gravity assist BVP algorithm was formulated. The mission constraints were strictly enforced, such as maintaining the closest approach above a minimum radius and below a maximum radius. An extension of the optimal powered gravity assist research is the development of a gravity assist BVP algorithm that utilizes an asymptote ΔV correction maneuver to produce ballistic gravity assist trajectory solutions. The efficient algorithm is tested with real interplanetary mission trajectory parameters and successfully converges upon ballistic gravity assists with improved performance compared to traditional methods. A hybrid approach is also presented, using the asymptote maneuver algorithm together with traditional gravity assist constraints to reach ballistic trajectory solutions more reliably, while improving computational performance.