An autonomous mission manager is being developed for use on CubeSats to perform proximity operations with other vehicles. The mission manager software is designed to run in real-time on a microprocessor used on a CubeSat. A simulation tool was developed that provides orbital dynamics and sensor measurements to test the mission manager software. A scenario was developed to demonstrate the control of a spacecraft from 1 km to 1 m to a target vehicle. Two small satellites were simulated in near-circular orbits around Earth at an approximate 400 km altitude. Each satellite is incorporated with simulated sensors and a Kalman filter. The simulation tool includes models for accelerometers and Global Positioning System receivers. Noise corruption is added to the modeled sensors to simulate imperfect knowledge. The simulation environment is capable of modeling Earth as a spherical or non-spherical body with spherical gravitational harmonics. Simulation parameters, such as the vehicle's initial states, Earth gravity model, and sensor noise are easily changed without recompiling the program through a simulation input file. / text

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The development of navigation filters that make use of robust estimation techniques is important due to the sensitivity of the typical minimum L2 norm techniques, such as the Kalman filter, to deviations in the assumed underlying probability distribution. In particular, those distributions with thicker tails than the Gaussian distribution can give rise to erratic filter performance and inconsistency of results. This dissertation discusses the development of an adaptive discrete-time robust nonlinear filtering technique based on a recursive form of Huber's mixed minimum L1/L2 norm approach to estimation, which is robust with respect to deviations from the assumed Gaussian error probability distributions inherent to the Kalman filter. This mixed norm approach is applied to a type of Sigma-Point Kalman filter, known as the Divided Difference Filter, which can capture second-order effects of nonlinearities in the system and measurement dynamics. Additionally, if these assumed parameters of the distribution differ greatly from the true parameters, then the filter can exhibit large errors and possibly divergence in nonlinear problems. This behavior is possible even if the true error distributions are Gaussian. To remedy these problems, adaptive filtering techniques have been introduced in order to automatically tune the Kalman filter by estimating the measurement and process noise covariances, however these techniques can also be highly sensitive to the nature of the underlying error distributions. The Huber-based formulations of the filtering problem also make some assumptions regarding the distribution, namely the approach considers a class of contaminated densities in the neighborhood of the Gaussian density. Essentially the method assumes that the statistics of the main Gaussian density are known, as well as the ratio or percentage of the contamination. The technique can be improved upon by the introduction of a method to adaptively estimate the noise statistics along with the state and state error covariance matrix. One technique in common use for adaptively estimating the noise statistics in real-time filtering applications is known as covariance matching. The covariance matching technique is an intuitively appealing approach in which the measurement noise and process noise covariances are determined in such a way that the true residual covariance matches the theoretically predicted covariance. The true residual covariance is approximated in real time using the sample covariance, over some finite buffer of stored residuals. The drawback to this approach is that the presence of outliers and non-Gaussianity can create problems of robustness with the use of the covariance matching technique. Therefore some additional steps must be taken to identify the outliers before forming the covariance estimates. In this dissertation, an adaptive scheme is proposed whereby the filter can estimate the process noise and measurement noise covariance matrices along with the state estimate and state estimate error covariance matrix. The adaptation technique adopts a robust approach to estimating these covariances that can resist the effects of outliers. The particular outlier identification method employed in this paper is based on quantities known as projection statistics, which utilize the sample median and median absolute deviation, and as a result are highly effective technique for multivariate outlier identification. These projection statistics are then employed as weights in the covariance matching procedure in order to reduce the influence of the outliers. The hybrid robust/adaptive nonlinear filtering methods introduced in this dissertation are applied to the problem of 6-DOF rendezvous navigation in elliptical orbit. The full nonlinear equations of relative motion are formulated in spherical coordinates centered on the target orbit. A relatively simple control law based on feedback linearization is used to track a desired rendezvous trajectory. The attitude dynamics are parameterized using Modified Rodrigues Parameters, which are advantageous for both control law development and estimation since they constitute a minimal 3-parameter attitude description. A switching technique which exploits the stereographic projection properties of the MRP coordinate is utilized to avoid singularities which inevitably arise in minimal attitude descriptions. This dissertation also introduces the proper covariance transformations associated with the singularity avoidance switching technique. An attitude control law based on backstepping is employed to track the target vehicle. A sensor suite consisting of a generic lidar or optical sensor, an Inertial Measurement Unit, consisting of accelerometers and gyroscopes, a star tracker, and a horizon sensor are utilized to provide measurement data to the navigation filters so that the chaser vehicle can estimate its relative state during the rendezvous maneuver. Several filters are implemented for comparison, including the Extended Kalman Filter, First and Second-Order Divided Difference Filters and Huber-based generalizations of these filters that include adaptive techniques for estimating the noise covariances. Monte-Carlo simulations are presented which include both Gaussian and non-Gaussian errors, including mismatches in the assumed noise covariances in the navigation filters in order to illustrate the benefits of the robust/adaptive nonlinear filters. Additionally, computational burdens of the various filters is compared. / Ph. D.

Orbital rendezvous enables spacecraft to perform missions to service satellites, remove space debris, resupply space stations, and return samples from other planets. These missions are often considered high risk due to concerns that the two spacecraft will collide if the maneuvering capability of one spacecraft is compromised by a fault.<br>In this thesis, a passive safety analysis is used to evaluate the probability that a fault that compromises maneuvering capability results in a collision. For a rendezvous<br>mission, the chosen approach trajectory, state estimation technique, and probability of collision calculation each impact the total collision probability of the mission. This<br>thesis presents a modular framework for evaluating the comparing the probability of collision of rendezvous mission design concepts.<br>Trade studies were performed using a baseline set of approach trajectories, and a Kalman Filter for relative state estimation and state estimate uncertainty. The state covariance matrix following each state update was used to predict the resulting probability of collision if a fault were to occur at that time. These trade studies emphasize that the biggest indicator of rendezvous mission risk is the time spent on a nominal intercept trajectory.

The Crew Exploration Vehicle (CEV) necessitates higher levels of automation than previous NASA vehicles due to program requirements for automation, including Automated Rendezvous and Docking (AR&D). Studies of spacecraft development often point to the locus of decision-making authority between humans and computers (i.e. automation) as a prime driver for cost, safety, and mission success. Therefore, a critical component in the CEV development is the determination of the correct level of automation. To identify the appropriate levels of automation and autonomy to design into a human space flight vehicle, NASA has created the Function-specific Level of Autonomy and Automation Tool (FLOAAT). This research develops a methodology for prototyping increased levels of automation for spacecraft rendezvous functions. This methodology was used to evaluate the accuracy of the FLOAAT-specified levels of automation, via prototyping. Two spacecraft rendezvous planning tasks were selected and then prototyped in Matlab using Fuzzy Logic (FL) techniques and existing Shuttle rendezvous trajectory algorithms. The prototyped functions are the determination of the maximum allowable Timeof- IGnition (TIG) slip for a rendezvous phasing burn and the evaluation of vehicle position relative to Transition initiation (Ti) position constraints. The methodology for prototyping rendezvous functions at higher levels of automation is judged to be a promising technique. The results of the prototype indicate that the FLOAAT recommended level of automation is reasonably accurate and that FL can be effectively used to model human decision-making used in spacecraft rendezvous. FL has many desirable attributes for modeling human decision-making, which makes it an excellent candidate for additional spaceflight automation applications. These conclusions are described in detail as well as recommendations for future improvements to the FLOAAT method and prototyped rendezvous functions.

The study of satellite relative motion has been of great historic interest, primarily due to its application to rendezvous, intercept, and docking maneuvers, between spacecraft in orbit about gravitational bodies, such as the Earth. Recent interest in the problem of satellite formation flight has also led to renewed effort in understanding the dynamics of relative motion. Satellite formations have been proposed for various tasks, such as deep-space interferometry, and terrestrial observation, among others. Oftentimes, the rich natural dynamics of the relative motion problem near a gravitational body are exploited to design formations of a specific geometry. Traditional analysis models relative motion under the assumptions of a circular reference orbit, linearized differential gravity field (small relative distance), and without environmental perturbations such as oblateness effects of the attracting body, and atmospheric drag. In this dissertation, the dynamics of the relative motion problem are studied when these assumptions are relaxed collectively. Consequently, the combined effects of nonlinearity, eccentricity, and Earth oblateness effects on relative motion, are studied. To this end, coupling effects between the various environmental perturbations are also accounted for. Five key problems are addressed - the development of a state transition matrix that accounts for eccentricity, nonlinearity, and oblateness effects; oblateness effects on averaged relative motion; eccentricity effects on formation design and planning; new analytical expressions for periodic relative motion that account for nonlinearity and eccentricity effects; and a solution to the optimal rendezvous problem near an eccentric orbit. The most notable feature of this dissertation, is that the solutions to the stated problems are completely analytical, and closed-form in nature. Use has been made of a generalized reversion of vector series, and several integral forms of Kepler’s equations, without any assumptions on the magnitude of the eccentricity of the reference orbit.

In current chip multi-processors (CMPs), the on-chip network is a major factor affecting overall system performance. Different kinds of communication protocols vary from different communication architectures of current SOC designs. For example, the AMBA is master-slave architecture, which transacts and communicates the data of between the two CORE (Master) through the Memory (Slave). The architecture cost long time for load and store with memory. Hence, this paper design and implement a Rendezvous protocol on AMBA architecture, which is called Rendezvous of Advanced High performance Bus (RAHB), to let two processors can communicate with each other without memory reference overheads. The RAHB is compatible with the AHB architecture, and add Rendezvous communication protocol in the AMBA architecture to perform the direct transmission of data. Without referring the memory, the RAHB can improve the efficiency of communication in multi-core. For experimental evaluation, we evaluate the performance between RAHB and AHB, RAHB speedup (B/s) is average up to 50% for different data length and performance up 30% to 40% for executing test program.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 183-185). Available also in a digital version from Dissertation Abstracts.

A new, novel numerical optimization algorithm is developed, tested, and used to solve difficult numerical problems from the field of astrodynamics. First, a brief review of optimization theory is presented and common numerical optimization techniques are discussed. Then, the new method, called the Learning Approach to Sampling Optimization (LA) is presented. Simple, illustrative examples are given to further emphasize the simplicity and accuracy of the LA method. Benchmark functions in lower dimensions are studied and the LA is compared, in terms of performance, to widely used methods. Three classes of problems from astrodynamics are then solved. First, the N - impulse orbit transfer and rendezvous problems are solved by using the LA optimization technique along with derived bounds that make the problem computationally feasible. This marriage between analytical and numerical methods allows an answer to be found for an order of magnitude greater number of impulses than are currently published. Next, the N -impulse work is applied to design periodic close encounters (PCE) in space. The encounters are defined as an open rendezvous, meaning that two spacecraft must be at the same position at the same time, but their velocities are not necessarily equal. The PCE work is extended to include N -impulses and other constraints, and new examples are given. Finally, a trajectory optimization problem is solved using the LA algorithm and comparing performance with other methods based on two models-with varying complexity-of the Cassini-Huygens mission to Saturn. The results show that the LA consistently outperforms commonly used numerical optimization algorithms.