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Digital polynomial and adaptive feedback control system techniques for large launch vehicle guidance, stability, and control
Algorithms are described for integration of Differential Global Positioning System (DGPS) data with Inertial Navigation System (INS) data to provide an integrated DGPS/INS navigation system. The objective is to establish the benefits that can be achieved through various levels of integration of DGPS with INS for precision navigation. An eight state Kalman filter integration was implemented in real-time on a twin turbo-prop transport aircraft to evaluate system performance during terminal approach and landing operations. A fully integrated DGPS/INS system is also presented which models accelerometer and rate-gyro measurement errors plus position, velocity, and attitude errors. The fully integrated system was implemented off-line using range-domain (seventeen-state) and position domain (fifteen-state) Kalman filters. Both filter integration approaches were evaluated using data collected during the flight test. Flight-test data consisted of measurements from a 5 channel Precision Code GPS receiver, a strap-down Inertial Navigation Unit (INU), and GPS satellite differential range corrections from a ground reference station. The aircraft was laser tracked to determine its true position. Results indicate that there is no significant improvement in positioning accuracy with the higher levels of DGPS/INS integration. All three systems provided high-frequency (e.g., 20 Hz) estimates of position and velocity. The fully integrated system provided estimates of inertial sensor errors which may be used to improve INS navigation accuracy should GPS become unavailable, and improved estimates of acceleration, attitude, and body rates which can be used for guidance and control. Precision Code DGPS/INS positioning accuracy (root-mean-square) was 1.0 m cross-track and 3.0 m vertical. (This AGARDograph was sponsored by the Guidance and Control Panel.)
A primary flight display format was integrated with the flight guidance and control system logic in support of various flight tests conducted with the NASA Transport Systems Research Vehicle B-737-100 airplane. The functional operation of the flight guidance mode control panel and the corresponding primary flight display formats are presented.
A computer program entitled "Marshall Aerospace Vehicle Representation in C II, (MAVERIC-II)" is a vehicle flight simulation program written primarily in the C programming language. It is written by James W. McCarter at NASA/Marshall Space Flight Center. The goal of the MAVERIC-II development effort is to provide a simulation tool that facilitates the rapid development of high-fidelity flight simulations for launch, orbital, and reentry vehicles of any user-defined configuration for all phases of flight. MAVERIC-II has been found invaluable in performing flight simulations for various Space Transportation Systems. The flexibility provided by MAVERIC-II has allowed several different launch vehicles, including the Saturn V, a Space Launch Initiative Two-Stage-to-Orbit concept and a Shuttle-derived launch vehicle, to be simulated during ascent and portions of on-orbit flight in an extremely efficient manner. It was found that MAVERIC-II provided the high fidelity vehicle and flight environment models as well as the program modularity to allow efficient integration, modification and testing of advanced guidance and control algorithms. In addition to serving as an analysis tool for techno logy development, many researchers have found MAVERIC-II to be an efficient, powerful analysis tool that evaluates guidance, navigation, and control designs, vehicle robustness, and requirements. MAVERIC-II is currently designed to execute in a UNIX environment. The input to the program is composed of three segments: 1) the vehicle models such as propulsion, aerodynamics, and guidance, navigation, and control 2) the environment models such as atmosphere and gravity, and 3) a simulation framework which is responsible for executing the vehicle and environment models and propagating the vehicle s states forward in time and handling user input/output. MAVERIC users prepare data files for the above models and run the simulation program. They can see the output on screen and/or store in files and examine the output data later. Users can also view the output stored in output files by calling a plotting program such as gnuplot. A typical scenario of the use of MAVERIC consists of three-steps; editing existing input data files, running MAVERIC, and plotting output results.
There are a number of approaches to advanced guidance and control (AG&C) that have the potential for achieving the goals of significantly increasing reusable launch vehicle (RLV) safety and reliability, and reducing the cost. This paper examines some approaches to entry guidance. An effort called Integration and Testing of Advanced Guidance and Control Technologies (ITAGCT) has recently completed a rigorous testing phase where these algorithms faced high-fidelity vehicle models and were required to perform a variety of representative tests. The algorithm developers spent substantial effort improving the algorithm performance in the testing. This paper lists the test cases used to demonstrate that the desired results are achieved, shows an automated test scoring method that greatly reduces the evaluation effort required, and displays results of the tests. Results show a significant improvement over previous guidance approaches. The two best-scoring algorithm approaches show roughly equivalent results and are ready to be applied to future reusable vehicle concepts.
Guidance and control techniques for terminal energy management and automatic horizontal landings of unpowered space shuttle vehicles
Electromagnetic sensors for guidance and control during spacecraft docking
We consider two spacecraft flying in formation to create interferometric synthetic aperture radar (InSAR). Several candidate orbits for such in InSar formation have been previously determined based on radar performance and Keplerian orbital dynamics. However, with out active control, disturbance-induced drift can degrade radar performance and (in the worst case) cause a collision. This study evaluates the feasibility of operating the InSAR spacecraft as a formation, that is, with inner-spacecraft sensing and control. We describe the candidate InSAR orbits, design formation guidance and control architectures and algorithms, and report the (Delta)(nu) and control acceleration requirements for the candidate orbits for several tracking performance levels. As part of determining formation requirements, a formation guidance algorithm called Command Virtual Structure is introduced that can reduce the (Delta)(nu) requirements compared to standard Leader/Follower formation approaches.
There are a number of approaches to advanced guidance and control that have the potential for achieving the goals of significantly increasing reusable launch vehicle (or any space vehicle that enters an atmosphere) safety and reliability, and reducing the cost. This paper examines some approaches to entry guidance. An effort called Integration and Testing of Advanced Guidance and Control Technologies has recently completed a rigorous testing phase where these algorithms faced high-fidelity vehicle models and were required to perform a variety of representative tests. The algorithm developers spent substantial effort improving the algorithm performance in the testing. This paper lists the test cases used to demonstrate that the desired results are achieved, shows an automated test scoring method that greatly reduces the evaluation effort required, and displays results of the tests. Results show a significant improvement over previous guidance approaches. The two best-scoring algorithm approaches show roughly equivalent results and are ready to be applied to future vehicle concepts.
An important new approach for vehicle guidance and control is based on the use of compact, low-mass, low-cost sensors integrated with the vehicle structure. Many advantages of this approach lead to new capabilities. However, the development of compact guidance and control sensors leads to a variety of fundamental physical problems associated with sensor sensitivity and noise. For example, as sensor size is reduced, it becomes necessary to improve the sensitivity of the sensor signal detection mechanism. These challenges to sensor development will be described. Recent developments at JPL, based on new position sensor principles such as electron tunneling, have produced a series of novel, ultra-high sensitivity micro-sensors and micro-instruments. Included among the applications demonstrated, are a high sensitivity micro-accelerometer and micro-seismometer. In this paper, the fundamental limits of conventional position sensors will be discussed and a new position sensor for advanced accelerometers will be described.
Guiding one state of linear dynamical system to prescribed rms terminal accuracy in presence of injection, measurement and engine-mechanization error
Reentry guidance and control in near-earth orbits and near escape velocity as well as in deep space and at velocities in excess of escape velocity
The problem of providing an appropriate method by which a human pilot interacts with an automated nap-of-the-earth rotorcraft guidance and control system is addressed. This problem is closely related to the broader question of what level and degree of automation is effective at reducing pilot workload during low-altitude flight missions requiring obstacle avoidance. A systematic approach for establishing the possible combinations of manual vs automatic authority over relevant guidance and control functions is first presented. From these possibilities, three candidate concepts are selected based upon their potential for practical implementation and reduction in pilot workload. This paper describes the selection of these three pilot-interaction concepts and the mathematical models for their implementation.
The problem of providing an appropriate method by which a human pilot interacts with an automated nap-of-the-Earth rotorcraft guidance and control system is addressed. This problem is closely related to the broader question of what level and degree of automation is effective at reducing pilot workload during low-altitude flight missions requiring obstacle avoidance. A systematic approach for establishing the possible combinations of manual versus automatic authority over relevant guidance and control functions is first presented. From these possibilities, three candidate concepts are selected; selection is based upon their potential for practical implementation and reduction in pilot workload. This paper describes the selection of these three pilot-interaction concepts and the mathematical models for their implementation.
The development and implementation of a dynamic digital computer simulator which may be used to evaluate aircraft performance when operating under the control and guidance of various navigation, landing, and flight control systems are discussed. The digital computer program may be used to simulate and evaluate the relationships and interactions between various factors such as the microwave landing system, avionics receivers and onboard processors, aircraft aerodynamics, aircraft automatic control systems, control surfaces, and wind and other external effects. The models used to represent aircraft aerodynamics, control system and control surfaces; weather and wind effects; and the microwave landing system are described. Example results are presented for a simulation of a Boeing 737 using two sample control systems while subjected to various atmospheric conditions and microwave landing system errors. The limitations and performance capabilities of these control systems are discussed in terms of their ability to utilize the microwave landing system signal.
Astronauts currently acquire ultrasound images on the International Space Station with the assistance of real-time remote guidance from an ultrasound expert in Mission Control. Remote guidance will not be feasible when significant communication delays exist during exploration missions beyond low-Earth orbit. For example, there may be as much as a 20- minute delay in communications between the Earth and Mars. Virtual-guidance, a pre-recorded audio-visual tutorial viewed in real-time, is a viable modality for minimally trained scanners to obtain diagnostically-adequate images of clinically relevant anatomical structures in an autonomous manner. METHODS: Inexperienced ultrasound operators were recruited to perform carotid artery (n = 10) and ophthalmic (n = 9) ultrasound examinations using virtual guidance as their only instructional tool. In the carotid group, each each untrained operator acquired two-dimensional, pulsed, and color Doppler of the carotid artery. In the ophthalmic group, operators acquired representative images of the anterior chamber of the eye, retina, optic nerve, and nerve sheath. Ultrasound image quality was evaluated by independent imaging experts. RESULTS: Eight of the 10 carotid studies were judged to be diagnostically adequate. With one exception the quality of all the ophthalmic images were adequate to excellent. CONCLUSION: Diagnostically-adequate carotid and ophthalmic ultrasound examinations can be obtained by untrained operators with instruction only from an audio/video tutorial viewed in real time while scanning. This form of quick-response-guidance, can be developed for other ultrasound examinations, represents an opportunity to acquire important medical and scientific information for NASA flight surgeons and researchers when trained medical personnel are not present. Further, virtual guidance will allow untrained personnel to autonomously obtain important medical information in remote locations on Earth where communication is difficult or absent.
The performance of Orion flight software, especially its GNC software, is being analyzed by running Monte Carlo simulations of Orion spacecraft flights. The simulated performance is analyzed for conformance with flight requirements, expressed as performance constraints. Flight requirements include guidance (e.g. touchdown distance from target) and control (e.g., control saturation) as well as performance (e.g., heat load constraints). The Monte Carlo simulations disperse hundreds of simulation input variables, for everything from mass properties to date of launch.We describe in this paper a sensitivity analysis tool (Critical Factors Tool or CFT) developed to find the input variables or pairs of variables which by themselves significantly influence satisfaction of requirements or significantly affect key performance metrics (e.g., touchdown distance from target). Knowing these factors can inform robustness analysis, can inform where engineering resources are most needed, and could even affect operations. The contributions of this paper include the introduction of novel sensitivity measures, such as estimating success probability, and a technique for determining whether pairs of factors are interacting dependently or independently. The tool found that input variables such as moments, mass, thrust dispersions, and date of launch were found to be significant factors for success of various requirements. Examples are shown in this paper as well as a summary and physics discussion of EFT-1 driving factors that the tool found.
G-Guidance is a robust G&C (guidance and control) algorithm developed under the small-body GN&C task. The G-Guidance scheme utilizes a model predictive control approach, along with a convexification of the governing dynamics, control constraints, and trajectory/state constraints. This report details an enhancement to the FY2005 G-Guidance algorithm; the addition of a fire-second approach helps to nullify velocity errors and hit desired final velocities much more precisely than the original Fire-first scheme developed in FY2005. The enhancement preserves the primary benefit of the algorithm, which is to ensure required thruster silent times during trajectory maneuvers. The fire-second scheme increases the versatility of the algorithm for missions employing G-Guidance. For instance, a landing sequence could employ the fire-second scheme to ensure a null final velocity at landing, whereas an ascent sequence could utilize a fire-first scheme to ensure there is no drift of the spacecraft toward the ground at the beginning of the maneuver. Examples are provided within to demonstrate a fire-first versus fire-second guidance scheme. As in the existing G-Guidance algorithm, the examples and schemes incorporate gravity models and thruster firing times into discrete dynamics that are solved as a optimal control problem to minimize fuel consumption or thruster energy expenditure.