This thesis presents the design of an attitude determination system for small satellites that automatically corrects for attitude drift. Existing attitude determination systems suffer from attitude drift due to the integration of noisy rate gyro sensors used to measure the change in attitude. This attitude drift leads to a gradual loss in attitude knowledge, as error between the estimated attitude and the actual attitude increases. In this thesis a Kalman filter is used to complete sensor fusion which combines sensor observations with a projected attitude based on the dynamics of the satellite. The system proposed in this thesis also utilizes a novel sensor called the stellar gyro to correct for the drift. The stellar gyro compares star field images taken at different times to determine orientation, and works in the presence of the sun and during eclipse. This device provides a relative attitude fix that can be used to update the attitude estimate provided by the Kalman filter, effectively compensating for drift. Simulink models are developed of the hardware and algorithms to model the effectiveness of the system. The Simulink models show that the attitude determination system is highly accurate, with steady state errors of less than 1 degree.

There is significant uncertainty in our knowledge of the Martian atmosphere and the aerodynamics of the Mars entry, descent, and landing (EDL) systems. These uncertainties result in conservatism in the design of the EDL vehicles leading to higher system masses and a broad range of performance predictions. Data from flight instrumentation onboard Mars EDL systems can be used to quantify these uncertainties, but the existing dataset is sparse and many parameters of interest have not been previously observable. Many past EDL reconstructions neither utilize statistical information about the uncertainty of the measured data nor quantify the uncertainty of the estimated parameters. Statistical estimation methods can blend together disparate data types to improve the reconstruction of parameters of interest for the vehicle. For example, integrating data obtained from aeroshell-mounted pressure transducers, inertial measurement unit, and radar altimeter can improve the estimates of the trajectory, atmospheric profile, and aerodynamic coefficients, while also quantifying the uncertainty in these estimates. These same statistical methods can be leveraged to improve current engineering models in order to reduce conservatism in future EDL vehicle design. The work in this thesis presents a comprehensive methodology for parameter reconstruction and uncertainty quantification while blending dissimilar Mars EDL datasets. Statistical estimation methods applied include the Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Filter. The estimators are applied in a manner in which the observability of the parameters of interest is maximized while using the sparse, disparate EDL dataset. The methodology is validated with simulated data and then applied to estimate the EDL performance of the 2012 Mars Science Laboratory. The reconstruction methodology is also utilized as a tool for improving vehicle design and reducing design conservatism. A novel method of optimizing the design of future EDL atmospheric data systems is presented by leveraging the reconstruction methodology. The methodology identifies important design trends and the point of diminishing returns of atmospheric data sensors that are critical in improving the reconstruction performance for future EDL vehicles. The impact of the estimation methodology on aerodynamic and atmospheric engineering models is also studied and suggestions are made for future EDL instrumentation.

19 Dec 2004. / Published through the Information Bridge: DOE Scientific and Technical Information. "IS-T 2626" Daniel Bryan Gazda. 12/19/2004. Report is also available in paper and microfiche from NTIS.

Made available in DSpace on 2016-08-17T14:52:45Z (GMT). No. of bitstreams: 1 Jose Alano Peres Abreu.pdf: 632239 bytes, checksum: 326cfda664cdb5244eb2f9f6331fb1fe (MD5) Previous issue date: 2002-12-13 / In this work, a new methodology of filtering data of paths of space vehicles is proposed H2 and H∞ saw state estimates and discreet. In that new methodology, it is obtained, initially, the solution of the problem of filtering of data of paths of space vehicles saw state estimate through the equations of the filter of Kalman for Predicted Estimators and Filtered Estimators. The problem is solved through the mathematical development of the equations of the filter of Kalman that has as main function, to find a state estimate that minimizes the least-squares error. The equations mathematics are used for the development of the algorithm of the filter of Kalman. The algorithm of filtering of Kalman has two basic functions: prediction and correction. In the prediction phase the initial estimates and updating of the time of sampling are given, while, in the correction phase they are updated the measures. It is applied, also, the new methodology proposed in the project of filtering of data of path of space vehicles H∞ saw state estimate through equations of robust filter. The robust filtering has as function to esteem a linear combination that minimizes the norm, that has the interpretation of the existence of earnings of maximum energy of the entrance for the exit. In addition, it is obtained a new algorithm for filtering of data of paths of space vehicles, now through state estimate. All the project procedures are cultured through some applied examples to systems of tracking of space vehicles. The results are compared and discussed. / Neste trabalho, é proposta uma metodologia de filtragem de dados de trajetórias de veículos espaciais via estimações de estado H2 e H∞ , discretos. Nessa metodologia, obtém-se, inicialmente, a solução do problema de filtragem de dados de trajetórias de veículos espaciais via estimação de estado H2 através das equações do filtro de Kalman para Estimadores Filtrados. O problema é resolvido através do desenvolvimento matemático das equações do filtro de Kalman que tem como objetivo principal encontrar uma estimação de estado que minimize o erro quadrático médio. As equações matemáticas são utilizadas para o desenvolvimento do algoritmo computacional do filtro de Kalman. O algoritmo de filtragem de Kalman tem duas funções básicas: predição e correção. Na fase de predição são dadas as estimativas iniciais e atualização do tempo de amostragem, enquanto que, na fase de correção são atualizadas as medidas. Aplica-se, também, a nova metodologia proposta no projeto de filtragem de dados de trajetória de veículos espaciais via estimação de estado H∞ através de equações do filtro de Kalman robusto. A filtragem robusta tem como objetivo principal estimar uma combinação linear que minimize a norma H∞ , que tem a interpretação da existência de ganho de energia máxima da entrada para a saída. Como contribuição, obtém-se um novo algoritmo computacional para filtragem de dados de trajetórias de veículos espaciais, agora através de estimação de estado H∞ . Todos os procedimentos de projeto são ilustrados através de alguns exemplos aplicados a sistemas de rastreamento de veículos espaciais. Os resultados são comparados e discutidos.

No description available.

This thesis presents a decentralized electromagnetic formation flying (EMFF) control method using frequency-multiplexed sinusoidal control signals. We demonstrate the EMFF control approach in open-loop and closed-loop control experiments using a single-degree-of-freedom testbed with an electromagnetic actuation system (EAS). The EAS sense the relative position and velocity between satellites and implement a frequency-multiplexed sinusoidal control signal. We use a laser-rangefinder device to capture the relative position and an ARM-based microcontroller to implement the closed-loop control algorithm. We custom-design and build the EAS that implements the formation control in one dimension. The experimental results in this thesis demonstrate the feasibility of the decentralized formation control algorithm between two satellites.

Several targeting algorithms are developed and analyzed for possible future use onboard a spacecraft. Each targeter is designed to determine the appropriate propulsive burn for translunar injection to obtain desired orbital parameters upon arrival at the moon. Primary design objectives are to minimize the computational requirements for each algorithm but also to ensure reasonable accuracy, so that the algorithm’s errors do not force the craft to conduct large mid-course corrections. Several levels of accuracy for dynamical models are explored, the convergence range and speed of each algorithm are compared, and the possible benefits of the Broyden and trust-region targeters are evaluated. These targeters provide a proof of concept for the feasibility of a translunar injection targeting algorithm. Anticipating some future improvements, these algorithms could serve as a viable alternative to uploading ground-based targeting solutions and bypass the problems of delays and disruptions in communication, enabling the craft to conduct a translunar injection burn autonomously.

Spacecraft fulfill a myriad of critical functions on orbit, from defense and intelligence to science, navigation, and telecommunication. Spacecraft can also cost several hundred millions of dollars to design and launch, and given that physical access for maintenance remains difficult if not impossible to date, designing high reliability and survivability into these systems is an engineering and financial imperative. While reliability is recognized as an essential attribute for spacecraft, little analysis has been done pertaining to actual field reliability of spacecraft and their subsystems. This thesis consists of two parts. The first part fills the gap in the current understanding of spacecraft failure behavior on orbit through extensive statistical analysis and modeling of anomaly and failure data of Earth-orbiting spacecraft. The second part builds on these results to develop a novel theoretical basis (interdependent multi-layer network approach) and algorithmic tools for the analysis of survivability of spacecraft and space-based networks. Space-based networks (SBNs) allow the sharing of on-orbit resources, such as data storage, processing, and downlink. Results indicate and quantify the incremental survivability improvement of the SBN over the traditional monolith architecture. A trade-space analysis is then conducted using non-descriptive networkable subsystems/technologies to explore survivability characteristics of space-based networks and help guide design choices.

Space debris growth and an influx in space traffic will create a need for increased space traffic management. Due to orbital population density and likely future growth, the implementation of a slot architecture to Sun-synchronous orbit is considered in order to mitigate conjunctions among active satellites. This paper furthers work done in Sun-synchronous orbit slot architecture design and focuses on two main aspects. First, an in-depth relative motion analysis of satellites with respect to their assigned slots is presented. Then, a method for developing a slot architecture from a specific set of user defined inputs is derived.

Aerocapture offers a near propellantless and quick method of orbit insertion at atmosphere bearing planetary destinations. Compared to conventional propulsive insertion, the primary advantage of using aerocapture is the savings in propellant mass which could be used to accommodate more useful payload. To protect the spacecraft from the aerodynamic heating during the maneuver, the spacecraft must be enclosed in a protective aeroshell or deployable drag device which also provides aerodynamic control authority to target the desired conditions at atmospheric exit. For inner planets such as Mars and Venus, aerocapture offers a very attractive option for inserting small satellites or constellations into very low circular orbits such as those used for imaging or radar observations. The large amount of propellant required for orbit insertion at outer planets such as Uranus and Neptune severely limits the useful payload mass that can delivered to orbit as well as the achievable flight time. For outer planet missions, aerocapture opens up an entirely new class of short time of flight trajectories which are infeasible with propulsive insertion. A systems framework for rapid conceptual design of aerocapture missions considering the interdependencies between various elements such as interplanetary trajectory and vehicle control performance for aerocapture is presented. The framework provides a step-by-step procedure to formulate an aerocapture mission starting from a set of mission objectives. At the core of the framework is the ``aerocapture feasibility chart", a graphical method to visualize the various constraints arising from control authority requirement, peak deceleration, stagnation-point peak heat rate, and total heat load as a function of vehicle aerodynamic performance and interplanetary arrival conditions. Aerocapture feasibility charts have been compiled for all atmosphere-bearing Solar System destinations for both lift and drag modulation control techniques. The framework is illustrated by its application to conceptual design of a Venus small satellite mission and a Flagship-class Neptune mission using heritage blunt-body aeroshells. The framework is implemented in the Aerocapture Mission Analysis Tool (AMAT), a free and open-source Python package, to enable scientists and mission designers perform rapid conceptual design of aerocapture missions. AMAT can also be used for rapid Entry, Descent, and Landing (EDL) studies for atmospheric probes and landers at any atmosphere-bearing destination.