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Optimal quadratic Liapunov function generating algorithm for estimating domain of equilibrium attraction of nonlinear star tracker attitude control systems for OAO stability
A digital computer algorithm for desensitized specific optimal system design for general time-invariant nonlinear systems is presented. The algorithm utilizes a technique of partitioning and uncoupling of the original specific closed-loop system state and sensitivity equations into linear, nonlinear, and control subsystems. Results of the application of the algorithm in the design of a Saturn V Launch Vehicle analog attitude control system and an LST image motion compensation digital controller are given.
The attitude determination (AD) system hardware of Seasat-A consists of two sun aspect sensors, two infrared (IR) horizon scanners, related electronics, telemetry, and science sensors. The earth horizon pulse signal sensing is accomplished by a variable thresholding method intended to self-adjust for earth radiance variations. The use of sun sensors rather than star trackers for yaw determination calls for estimation rather than measurement of yaw attitude about 50% of mission time. Features of the yaw interpolation algorithm include adaptability and automatic incorporation of time-varying confidence levels of various data sources. The performance accuracy of the AD system was assessed, where it is found that the most important error source is the variation in earth atmospheric radiance. Analysis of science instrument sensitivity to AD uncertainties was performed, and the most sensitive instrument, the scatterometer system, shows only gradual degradation of performance in the vicinity of the AD accuracy requirement.
The on-off reaction jet control system is often used for attitude and orbital maneuvering of various spacecraft. Future space vehicles such as the orbital transfer vehicles, orbital maneuvering vehicles, and space station will extensively use reaction jets for orbital maneuvering and attitude stabilization. The proposed robust fuel- and time-optimal control algorithm is used for a three-mass spacing model of flexible spacecraft. A fuel-efficient on-off control logic is developed for robust rest-to-rest maneuver of a flexible vehicle with minimum excitation of structural modes. The first part of this report is concerned with the problem of selecting a proper pair of jets for practical trade-offs among the maneuvering time, fuel consumption, structural mode excitation, and performance robustness. A time-optimal control problem subject to parameter robustness constraints is formulated and solved. The second part of this report deals with obtaining parameter insensitive fuel- and time- optimal control inputs by solving a constrained optimization problem subject to robustness constraints. It is shown that sensitivity to modeling errors can be significantly reduced by the proposed, robustified open-loop control approach. The final part of this report deals with sliding mode control design for uncertain flexible structures. The benchmark problem of a flexible structure is used as an example for the feedback sliding mode controller design with bounded control inputs and robustness to parameter variations is investigated.
When taking aerial images, it is important to know locations of specific points of interest in an Earth-centered coordinate system (latitude, longitude, height). The correspondence between a pixel location in the image and its Earth coordinate is known as georegistration. There are two main technical challenges arising in the intended application. The first is that no known features are assumed to be available in any of the images. The second is that the intended applications are real time. Here, images are taken at regular intervals (i.e. once per second), and it is desired to make decisions in real time based on the geolocation of specific objects seen in the images as they arrive. This is in sharp contrast to most current methods for geolocation that operate "after-the-fact" by processing, on the ground, a database of stored images using computationally intensive methods. The solution is a nonlinear estimation algorithm that combines processed realtime camera images with vehicle position and attitude information ob tained from an onboard GPS receiver. This approach provides accurate georegistration estimates (latitude, longitude, height) of arbitrary features and/or points of interest seen in the camera images. This solves the georegistration problem at the modest cost of augmenting the camera information with a GPS receiver carried onboard the vehicle.
The end-to-end verification of a spacecraft photovoltaic power generation system requires light! Specifically, the standard practice for doing so is the Large Area Pulsed Solar Simulation (LAPSS). A LAPSS test can characterize a photovoltaic system's efficiency via its response to rapidly applied impulses of simulated sunlight. However, a Class D program on a constrained budget and schedule may not have the resources to ship an entire satellite for a LAPSS test alone. Such was the case with the Lunar Atmospheric and Dust Environment Explorer (LADEE) program, which was also averse to the risk of hardware damage during shipment. When the Electrical Power System (EPS) team was denied a spacecraft-level LAPSS test, the lack of an end-to-end power generation test elevated to a project-level technical risk. The team pulled together very limited resources to not only eliminate the risk, but build a process to monitor the health of the system through mission operations. We discuss a process for performing a low-cost, end-to-end test of the LADEE photovoltaic system. The approach combines system-level functional test, panel-level performance results, and periodic inspection (and repair) up until launch. Following launch, mission operations tools are utilized to assess system performance based on a scant amount of data. The process starts in manufacturing at the subcontractor. The panel manufacturer provides functional test and LAPSS data on each individual panel. We apply an initial assumption that the per-panel performance is sufficient to meet the power generation requirements. The manufacturer's data is also carried as the performance allocation for each panel during EPS system modeling and initial mission operations. During integration and test, a high-power, professional theater lamp system provides simulated sunlight to each panel on the spacecraft, thereby permitting a true end-to-end system test. A passing test results in a step response to nearly full-rated current at the appropriate solar array switch in the power system. A metal-halide bulb, infrared imagers, and onboard spacecraft measurements are utilized to minimize risk of thermal damage during test. Data is provided to support test results for both passing and marginal panels. Prior to encapsulation in the launch vehicle, each panel is inspected for damage by the panel manufacturer. Cracked cells or other damage is amended on-site. Because the photovoltaic test system is inexpensive and portable, each repaired panel can be re-verified immediately. Post-launch, the photovoltaic system is again characterized for per-panel deviations from the manufacturer's performance test. This proved especially tricky as the LADEE spacecraft performs only one current measurement on the entire array. The algorithm for Matlab tools to assess panel performance based on spacecraft attitude is discussed. While not as precise and comprehensive as LAPSS, the LADEE approach leverages minimal resources into an ongoing assessment program that can be applied through numerous stages of the mission. The project takes a true Class D approach in assessing the technical value of a spacecraft level performance test versus the programmatic risk of shipping the spacecraft to another facility. The resources required are a fraction of that for a LAPSS test, and is easy to repeat. Further, the test equipment can be handed down to future projects without building an on-site facility.
The MER (Mars Exploration Rover) Attitude and Pointing System Simulation Testbed Environment (RAPSSTER) provides a simulation platform used for the development and test of GNC (guidance, navigation, and control) flight algorithm designs for the Mars rovers, which was specifically tailored to the MERs, but has since been used in the development of rover algorithms for the Mars Science Laboratory (MSL) as well. The software provides an integrated simulation and software testbed environment for the development of Mars rover attitude and pointing flight software. It provides an environment that is able to run the MER GNC flight software directly (as opposed to running an algorithmic model of the MER GNC flight code). This improves simulation fidelity and confidence in the results. Further more, the simulation environment allows the user to single step through its execution, pausing, and restarting at will. The system also provides for the introduction of simulated faults specific to Mars rover environments that cannot be replicated in other testbed platforms, to stress test the GNC flight algorithms under examination. The software provides facilities to do these stress tests in ways that cannot be done in the real-time flight system testbeds, such as time-jumping (both forwards and backwards), and introduction of simulated actuator faults that would be difficult, expensive, and/or destructive to implement in the real-time testbeds. Actual flight-quality codes can be incorporated back into the development-test suite of GNC developers, closing the loop between the GNC developers and the flight software developers. The software provides fully automated scripting, allowing multiple tests to be run with varying parameters, without human supervision.
The Galileo sun acquisition algorithm is used to align the spacecraft antenna with the sun in order to determine spacecraft attitude. It is also used to estimate the spin rate when the spacecraft antenna is not sun oriented, and is capable of performing a rhumb line turn maneuver in the case of two gyro failures. The design of the algorithm is presented in detail along with software implementation at the flowchart level. The six major portions of the algorithm are considered: initialization, sensor measurement mapping, path selection logic, sun detection logic, termination logic, and burn command generation. Analysis is performed to determine the major parameters of the algorithm, and results are verified by computer simulations.
This paper describes MAVeN (Minimal State Augmentation Algorithm for Vision-Based Navigation), which is a new algorithm for vision-based navigation that has only 21 states, yet is able to track features in successive camera images and use them to propagate estimates of the spacecraft position and velocity. The filter dimension drops to 12 if attitude information is already available. The low filter dimension makes MAVeN a very reliable and practical algorithm for real-time flight implementation. The main idea is to project observed features onto a rough shape model of the ground surface, which are then used by the filter as pseudo-landmarks. The shape model is assumed to be known beforehand, as would be obtained from prior surveillance of the landing site from orbit. MAVeN does not require pre-mapped landmarks, so it is able to navigate terrain that has not been previously observed up close. This property is especially important for close proximity operations in small body missions where ground surface features are being seen for the first time at close range. MAVeN is also able to hover motionless above the ground without position error growth, which is unusual for this class of vision-based navigation algorithms.
The advantages of inducing a constant spin rate on a spacecraft are well known. A variety of science missions have used this technique as a relatively low cost method for conducting science. Starting in the late 1970s, NASA focused on building spacecraft using 3-axis control as opposed to the single-axis control mentioned above. Considerable effort was expended toward sensor and control system development, as well as the development of ground systems to independently process the data. As a result, spinning spacecraft development and their resulting ground system development stagnated. In the 1990s, shrinking budgets made spinning spacecraft an attractive option for science. The attitude requirements for recent spinning spacecraft are more stringent and the ground systems must be enhanced in order to provide the necessary attitude estimation accuracy. Since spinning spacecraft (SC) typically have no gyroscopes for measuring attitude rate, any new estimator would need to rely on the spacecraft dynamics equations. One estimation technique that utilized the SC dynamics and has been used successfully in 3-axis gyro-less spacecraft ground systems is the pseudo-linear Kalman filter algorithm. Consequently, a pseudo-linear Kalman filter has been developed which directly estimates the spacecraft attitude quaternion and rate for a spinning SC. Recently, a filter using Markley variables was developed specifically for spinning spacecraft. The pseudo-linear Kalman filter has the advantage of being easier to implement but estimates the quaternion which, due to the relatively high spinning rate, changes rapidly for a spinning spacecraft. The Markley variable filter is more complicated to implement but, being based on the SC angular momentum, estimates parameters which vary slowly. This paper presents a comparison of the performance of these two filters. Monte-Carlo simulation runs will be presented which demonstrate the advantages and disadvantages of both filters.
In order to prepare for the era of autonomous navigation, this paper first examines existing techniques for spacecraft navigation by describing the basic elements of the Mark-I and II navigation technologies. The remainder of the paper deals with identification of criteria which necessitate onboard navigation, and with promising solutions now under consideration. An attractive solution possibility is proposed which utilizes narrow-angle optics, a charge-coupled device area-array image sensor, a small advanced onboard flight computer, and a simplified set of software algorithms. Onboard commands would be derived and sent to the appropriate spacecraft attitude control, propulsion, and science platform pointing subsystems. The proposed autonomous navigation system does not address all mission options, but focuses primarily on precision approach trajectory control and adaptive science instrument pointing for asteroid, cometary, and outer planet satellite missions.
A discussion of the generation of the best estimate trajectory (BET) of the first Space Shuttle Orbiter entry flight is presented. The BET defines a time history of the state, attitude, and atmospheric relative parameters throughout the Shuttle entry from an altitude of approximately 183 km to rollout. The inertial parameters were estimated utilizing a weighted least squares batch filter algorithm. Spacecraft angular rate and acceleration data derived from the Inertial Measurement Unit were utilized to predict the state and attitude which was constrained in a weighted least squares process to fit external tracking data consisting of ground based S-band and C-band data. Refined spacecraft altitude and velocity during and post rollout were obtained by processing artificial altimeter and Doppler data. The BET generation process is discussed. Software and data interface discussions are included. The variables and coordinate systems utilized are defined. STS-1 mission peculiar inputs are summarized. A listing of the contents of the actual BET is provided.
Information necessary to understand the Autonomous Attitude Determination System (AADS) is presented. Topics include AADS requirements, program structure, algorithms, and system generation and execution.
The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.
The SPAce Readiness Coherent Lidar Experiment (SPARCLE) mission was proposed as a low cost technology demonstration mission, using a 2-micron, 100-mJ, 6-Hz, 25-cm, coherent lidar system based on demonstrated technology. SPARCLE was selected in late October 1997 to be NASA's New Millennium Program (NMP) second earth-observing (EO-2) mission. To maximize the success probability of SPARCLE, NASA/MSFC desired expert guidance in the areas of coherent laser radar (CLR) theory, CLR wind measurement, fielding of CLR systems, CLR alignment validation, and space lidar experience. This led to the formation of the NASA/MSFC Coherent Lidar Technology Advisory Team (CLTAT) in December 1997. A threefold purpose for the advisory team was identified as: 1) guidance to the SPARCLE mission, 2) advice regarding the roadmap of post-SPARCLE coherent Doppler wind lidar (CDWL) space missions and the desired matching technology development plan 3, and 3) general coherent lidar theory, simulation, hardware, and experiment information exchange. The current membership of the CLTAT is shown. Membership does not result in any NASA or other funding at this time. We envision the business of the CLTAT to be conducted mostly by email, teleconference, and occasional meetings. The three meetings of the CLTAT to date, in Jan. 1998, July 1998, and Jan. 1999, have all been collocated with previously scheduled meetings of the Working Group on Space-Based Lidar Winds. The meetings have been very productive. Topics discussed include the SPARCLE technology validation plan including pre-launch end-to-end testing, the space-based wind mission roadmap beyond SPARCLE and its implications on the resultant technology development, the current values and proposed future advancement in lidar system efficiency, and the difference between using single-mode fiber optical mixing vs. the traditional free space optical mixing. attitude information from lidar and non-lidar sensors, and pointing knowledge algorithms will meet this second requirement. The topic of this paper is the pre-launch demonstration of the first requirement, adequate sensitivity of the SPARCLE lidar.
The objective of this work is to reshape a nacelle to achieve a specified nacelle pressure distribution. The nacelle may be either isolated or installed on an airplane. There are no restrictions on the attitude (toe, incidence, and roll) and position of the nacelle. The design algorithm is coupled to two different multi-block 3-D Navier Stokes flow solvers. The coupling between design and analysis is automated to the point where the design proceeds with minimal user input.
The Magnetospheric Multiscale (MMS) mission, launched on March 13, 2015, is the fourth mission of NASA's Solar Terrestrial Probe program. The MMS mission consists of four identically instrumented observatories that function as a constellation to provide the first definitive study of magnetic reconnection in space. Since it is frequently desirable to isolate electric and magnetic field sensors from stray effects caused by the spacecraft's core-body, the suite of instruments on MMS includes six radial and two axial instrument-booms with deployed lengths ranging from 5-60 meters (see Figure 1). The observatory is spin-stabilized about its positive z-axis with a nominal rate slightly above 3 rev/min (RPM). The spin is also used to maintain tension in the four radial wire-booms. Each observatory's Attitude Control System (ACS) consists of digital sun sensors, star cameras, accelerometers, and mono-propellant hydrazine thrusters-responsible for orbital adjustments, attitude control, and spin adjustments. The sections that follow describe performance requirements, the hardware and algorithms used for 6-DOF estimation, and then similarly for 6-DOF control. The paper concludes with maneuver performance based on both simulated and on-orbit telem.
A method for accurately initializing spacecraft attitude after release from the Orbiter is described. It is noted that the method is suitable for an autonomous ascent to mission orbit. Test results are given from a FORTRAN simulation of the estimation algorithm using measurement data from a detailed spacecraft dynamics simulation program. The technique here is orbital yaw-gyrocompassing. Attitude is estimated through a Kalman filter, using pitch and roll measurements from an earth sensor, while gyro data provide the system dynamics information. In the tests described, gyro and earth sensor data are generated by an existing control system simulation of earth-search and yaw-gyrocompassing attitude dynamics; they include realistic errors such as delays, random noise and quantization effects. The estimated attitude history is compared with the true attitude history from the simulation program to assess the accuracy and convergence of the filter in the presence of noisy measurements and disturbances, including thruster firings for momentum control. It is noted that since the earth sensor provides direct measurements of pitch and roll, the main criterion of filter performance is yaw accuracy.