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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Neuromorphic learning of continuous-valued mappings from noise-corrupted data

The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neurocontroller with good noise-filtering properties. In the presence of plant noise, the neurocontroller is found to be more stable than the teacher. A novel perspective on the application of neural network technology to control engineering is presented.

Troudet, T.↗
NASA NTRS1990

Knowledge acquisition and representation for the Systems Test and Operations Language (STOL) Intelligent Tutoring System (ITS)

This presentation concentrates on knowledge acquisition and its application to the development of an expert module and a user interface for an Intelligent Tutoring System (ITS). The Systems Test and Operations Language (STOL) ITS is being developed to assist NASA control center personnel in learning a command and control language as it is used in mission operations rooms. The objective of the tutor is to impart knowledge and skills that will permit the trainee to solve command and control problems in the same way that the STOL expert solves those problems. The STOL ITS will achieve this object by representing the solution space in such a way that the trainee can visualize the intermediate steps, and by having the expert module production rules parallel the STOL expert's knowledge structures.

Seamster, Thomas L.↗
NASA NTRS1994

Permanent superconducting magnets for space applications

Work has been done to develop superconducting trapped field magnets (TFM's) and to apply them to a bumper-tether device for magnetic docking of spacecraft. The quality parameters for TFM's are J(c), the critical current of the superconductor, and d, the diameter of the superconducting tile. During this year we have doubled d, for production models, from 1 cm to 2 cm. This was done by means of seeding, an improved temperature profile in processing, and the addition of 1 percent Pt to the superconductor chemistry. Using these tiles we have set increasing records for the fields' permanent magnets. Magnets fabricated from old 1 cm tiles trapped 1.52 Tesla at 77K, 4.0T at 65K and 7.0T at 55K. The second of these fields broke a 17 year old record set at Stanford. The third field broke our own record. More recently using 2 cm tiles, we have trapped 2.3T at 77K, and 5.3T at 65K. We expect to trap lOT at 55K in this magnet in the near future. We have also achieved increases in J(c) using a method we developed for seeding U-235, and subsequently bombarding with neutrons. This method doubles J(c). We have not yet fabricated magnets from these tiles. During this year we have increased production yields from 15 percent to 95 percent. We have explored the properties of a magnetic bumper-tether for spacecraft. We have measured the bumper forces, and their dependence on time, distance, and the field of the ordinary ferromagnet (used together with a TFM). We have accounted for 85 percent of the collision energy, and its transformation to magnetic energy and heat energy. We have learned to control the relative bumper and tether forces by controlling TFM and ferromagnetic field strengths.

Weinstein, Roy↗
NASA NTRS1990

Biofeedback for Better Vision

Biofeedtrac, Inc.'s Accommotrac Vision Trainer, invented by Dr. Joseph Trachtman, is based on vision research performed by Ames Research Center and a special optometer developed for the Ames program by Stanford Research Institute. In the United States, about 150 million people are myopes (nearsighted), who tend to overfocus when they look at distant objects causing blurry distant vision, or hyperopes (farsighted), whose vision blurs when they look at close objects because they tend to underfocus. The Accommotrac system is an optical/electronic system used by a doctor as an aid in teaching a patient how to contract and relax the ciliary body, the focusing muscle. The key is biofeedback, wherein the patient learns to control a bodily process or function he is not normally aware of. Trachtman claims a 90 percent success rate for correcting, improving or stopping focusing problems. The Vision Trainer has also proved effective in treating other eye problems such as eye oscillation, cross eyes, and lazy eye and in professional sports to improve athletes' peripheral vision and reaction time.

Source record↗
NASA NTRS2004

Smart Camera Technology Increases Quality

When it comes to real-time image processing, everyone is an expert. People begin processing images at birth and rapidly learn to control their responses through the real-time processing of the human visual system. The human eye captures an enormous amount of information in the form of light images. In order to keep the brain from becoming overloaded with all the data, portions of an image are processed at a higher resolution than others, such as a traffic light changing colors. changing colors. In the same manner, image processing products strive to extract the information stored in light in the most efficient way possible. Digital cameras available today capture millions of pixels worth of information from incident light. However, at frame rates more than a few per second, existing digital interfaces are overwhelmed. All the user can do is store several frames to memory until that memory is full and then subsequent information is lost. New technology pairs existing digital interface technology with an off-the-shelf complementary metal oxide semiconductor (CMOS) imager to provide more than 500 frames per second of specialty image processing. The result is a cost-effective detection system unlike any other.

Source record↗
NASA NTRS2008

Autonomous Formation Flight: Project Overview

Objectives: a) Map the vortex effects; b) Formation Auto-Pilot Requirements. Two NASA F/A-18 aircraft in formation: a) NASA 845 Systems Research Aircraft; b) NASA 847 Support Aircraft. Flight Conditions: M = 0.56, 25000 feet (Subsonic condition); b) M = 0.86, 36000 feet (Transonic condition). Nose-To-Tail (N2T) Distances: 20, 55, 110 and 190 feet. Lessons learned: a) Controllable flight in vortex is possible with pilot feedback (displays); b) Position hold at best C(sub D), is attainable; c) Best drag location is close to max rolling moment; e) Drag reductions demonstrated up to 22% (WFE up to 20%); f) Induced drag results compare favorably with simple prediction model; g) "Sweet Spot" (lateral & vertical area > 25%) is larger than predicted; h) Larger wing overlaps result in sign reversals in roll, yaw; i) As predicted, favorable effects degrade gradually with increased nose-to-tail distances after peaking at 3 span lengths aft; and j) Demonstrated - over 100 N mi (>15%) range improvement and 650 lbs (14%) fuel savings on actual simulated F/A-18 cruise mission.

Cole, Jennifer↗
NASA NTRS2015

Who's Got the Bridge? - Towards Safe, Robust Autonomous Operations at NASA Langley's Autonomy Incubator

NASA aeronautics research has made decades of contributions to aviation. Both aircraft and air traffic management (ATM) systems in use today contain NASA-developed and NASA sponsored technologies that improve safety and efficiency. Recent innovations in robotics and autonomy for automobiles and unmanned systems point to a future with increased personal mobility and access to transportation, including aviation. Automation and autonomous operations will transform the way we move people and goods. Achieving this mobility will require safe, robust, reliable operations for both the vehicle and the airspace and challenges to this inevitable future are being addressed now in government labs, universities, and industry. These challenges are the focus of NASA Langley Research Center's Autonomy Incubator whose R&D portfolio includes mission planning, trajectory and path planning, object detection and avoidance, object classification, sensor fusion, controls, machine learning, computer vision, human-machine teaming, geo-containment, open architecture design and development, as well as the test and evaluation environment that will be critical to prove system reliability and support certification. Safe autonomous operations will be enabled via onboard sensing and perception systems in both data-rich and data-deprived environments. Applied autonomy will enable safety, efficiency and unprecedented mobility as people and goods take to the skies tomorrow just as we do on the road today.

Allen, B. Danette↗
NASA NTRS2006

Improving Grasp Skills Using Schema Structured Learning

Abstract In the control-based approach to robotics, complex behavior is created by sequencing and combining control primitives. While it is desirable for the robot to autonomously learn the correct control sequence, searching through the large number of potential solutions can be time consuming. This paper constrains this search to variations of a generalized solution encoded in a framework known as an action schema. A new algorithm, SCHEMA STRUCTURED LEARNING, is proposed that repeatedly executes variations of the generalized solution in search of instantiations that satisfy action schema objectives. This approach is tested in a grasping task where Dexter, the UMass humanoid robot, learns which reaching and grasping controllers maximize the probability of grasp success.

Platt, Robert↗
NASA NTRS1994

A fuzzy classifier system for process control

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

Karr, C. L.↗
NASA NTRS2018

Spacecraft Micro-Vibration: A Survey of Problems, Experiences, Potential Solutions, and Some Lessons Learned

Predicting, managing, controlling, and testing spacecraft micro-vibrations due to on-board internal disturbance sources is a formidable multi-disciplinary systems engineering challenge, especially for those observatories hosting extremely sensitive optical sensor payloads with stringent requirements on allowable Line-of-Sight (LOS) jitter. In this paper some specific spacecraft micro-vibration engineering challenges will be introduced and described. Technical background context is provided with the inclusion of several illustrative examples of NASA and ESA missions (both past and present) where dynamic interactions have to be addressed and which have demanding payload instrument LOS jitter requirements. A general modeling, analysis, simulation, and test approach to address and solve the overall problem of spacecraft micro-vibrations is outlined. Recommended rules of thumb are presented to provide guidance for analysts on where to initiate and how to approach a new spacecraft micro-vibration design problem. A set of experience based spacecraft micro-vibration lessons learned are also presented in the hope they can be leveraged on new system development projects to help overcome unfamiliarity with previously identified micro-vibration technical pitfalls and challenges.

Alvarez-Salazar, Oscar S.↗
NASA NTRS2018

Spacecraft Micro-Vibration: A Survey of Problems, Experiences, Potential Solutions, and Some Lessons Learned

Predicting, managing, controlling, and testing spacecraft microvibrations due to on-board internal disturbance sources is a formidable multi-disciplinary systems engineering challenge, especially for those observatories hosting extremely sensitive optical sensor payloads with stringent requirements on allowable line-of-sight jitter. In this paper some specific spacecraft micro-vibration engineering challenges will be introduced and described. Technical background context is provided with the inclusion of several illustrative examples of NASA and European Space Agency missions (both past and present) where dynamic interactions have to be addressed and which have demanding payload instrument line-of-sight jitter requirements. A general modeling, analysis, simulation, and test approach to address and solve the overall problem of spacecraft micro-vibrations is outlined. Recommended rules of thumb are presented to provide guidance for analysts on where to initiate and how to approach a new spacecraft micro-vibration design problem. A set of experience-based spacecraft micro-vibration lessons learned are also presented in the hope they can be leveraged on new system development projects to help overcome unfamiliarity with previously identified microvibration technical pitfalls and challenges.

Dennehy, Cornelius↗
NASA NTRS2010

Lessons Learned from the Node 1 Atmosphere Control and Storage and Water Recovery and Management Subsystem Design

Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Atmosphere Control and Storage (ACS) and Water Recovery and Management (WRM) subsystems and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs.

Williams, David E.↗
NASA NTRS2011

Lessons Learned from the Node 1 Atmosphere Control and Storage and Water Recovery and Management Subsystem Design

Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Atmosphere Control and Storage (ACS) and Water Recovery and Management (WRM) subsystems and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs.

Williams, David E.↗
NASA NTRS2010

Challenges and Lessons Learned From Resurrecting a Legacy Research Flight Controller

Resurrecting the legacy Inner Loop Thrust Vectoring research flight controller to investigate the tail shock region brought unique challenges. This report documents these challenges and lessons learned from a stability and controls perspective. The flight test approach for flight envelope expansion and probing tests, as well as limited flight test results, are presented. Recent advances in sonic boom reduction technology have contributed to a resurgent interest in civilian supersonic cruise flight. These advances have focused only on fore body shaping, however, and little, if any, experimental flight data are available to develop and validate design tools for the tail shock region. In January of 2009, the NASA Dryden Flight Research Center completed research flights to investigate the tail shock region of a highly modified F-15 aircraft by probing the shock waves around it, using another F-15 aircraft. To adjust the lift distribution and plume shape, a decade-old research flight controller from the Inner Loop Thrust Vectoring project was required. To investigate the tail shock region, the lift distribution was changed by adjusting the canard position, and the plume shape was changed by adjusting the nozzle area and thrust vectoring.

Dees, Ray A.↗
NASA NTRS2018

Cassini Spacecraft Attitude Control System: Flight Performance and Lessons Learned, 1997-2017

A sophisticated interplanetary spacecraft, Cassini/Huygens was launched on October 15, 1997. Since achieving orbit at Saturn in 2004, Cassini has collected science data throughout its four-year prime mission (2004–08), and has since been approved for first and second extended missions through September 2017. The Cassini Attitude and Articulation Control Subsystem (AACS) is perhaps the spacecraft subsystem that must satisfy the most mission and science pointing requirements. Since launch, the performance of the Cassini AACS design has been superb. All key mission and science requirements are met with significant margins. An overview of the flight performance of the Cassini attitude control system as well as AACS mission operation-centric lessons learned, from launch to 2017, are described by topics. Many of these lessons learned should be applicable to the safe operations of other interplanetary missions. Processes taken by the AACS operation team to guard against “human” errors are also outlined in this paper.

Lee, Allan Y.↗
NASA NTRS2006

Learning Grasp Context Distinctions that Generalize

Control-based approaches to grasp synthesis create grasping behavior by sequencing and combining control primitives. In the absence of any other structure, these approaches must evaluate a large number of feasible control sequences as a function of object shape, object pose, and task. This work explores a new approach to grasp synthesis that limits consideration to variations on a generalized localize-reach-grasp control policy. A new learning algorithm, known as schema structured learning, is used to learn which instantiations of the generalized policy are most likely to lead to a successful grasp in different problem contexts. Two experiments are described where Dexter, a bimanual upper torso, learns to select an appropriate grasp strategy as a function of object eccentricity and orientation. In addition, it is shown that grasp skills learned in this way can generalize to new objects. Results are presented showing that after learning how to grasp a small, representative set of objects, the robot's performance quantitatively improves for similar objects that it has not experienced before.

Platt, Robert↗
NASA NTRS2018

International Space Station Passive Thermal Control System Top Ten Lessons-Learned

Final document not an Abstract attached. The International Space Station (ISS) has been on-orbit for nearly 20 years, and there have been numerous technical challenges along the way from design to assembly to on-orbit anomalies and repairs. The Passive Thermal Control System (PTCS) management team has been a key player in successfully dealing with these challenges. The PTCS team performs thermal analysis in support of design and verification, launch and assembly constraints, integration, sustaining engineering, failure response, and model validation. This analysis is a significant body of work and provides a unique opportunity to compile a wealth of real world engineering and analysis knowledge and the corresponding lessons-learned. The PTCS lessons encompass the full life cycle of flight hardware from design to on-orbit performance and sustaining engineering. These lessons can provide significant insight for new projects and programs. Key areas to be presented include thermal model fidelity, verification methods, analysis uncertainty, and operations support.

Iovine, John V.↗