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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Lessons Learned from the Hubble Space Telescope (HST) Contamination Control Program

Over the past two decades, the Hubble Space Telescope (HST) Contamination Control Program has evolved from a ground-based integration program to a space-based science-sustaining program. The contamination controls from the new-generation Scientific Instruments and Orbital Replacement Units were incorporated into the HST Contamination Control Program to maintain scientific capability over the life of the telescope. Long-term on-orbit scientific data has shown that these contamination controls implemented for the instruments, Servicing Mission activities (Orbiter, Astronauts, and mission), and on-orbit operations successfully protected the HST from contamination and the instruments from self-contamination.

Hansen, Patricia A.↗
NASA NTRS2018

A Learn-To-Fly Approach for Adaptively Tuning Flight Control Systems

A method is presented for adaptively tuning feedback control gains in a ight control sys- tem to achieve desired closed-loop performance. The method combines efficient parameter estimation for identifying closed-loop dynamics models, with online nonlinear optimization for sequentially perturbing and updating control gains to improve performance. Prior in- formation on stability and control derivatives is not needed, nor is any knowledge about the control system architecture. Following convergence, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear ight dynamics simulation of the NASA FASER airplane. Convergence was attained using five piloted maneuvers that spanned approximately one minute of ight test time. Although demonstrated for a relatively simple case, the method is general and can be applied to other aircraft, axes, performance metrics, and control systems.

Grauer, Jared A.↗
NASA NTRS2021

A Learn-to-Fly Approach for Adaptively Tuning Flight Control Systems

A method is presented for adaptively tuning feedback control gains in a flight control system to achieve desired closed-loop performance. The method combines efficient aircraft parameter estimation, for identifying closed-loop dynamics models, with online nonlinear optimization, for sequentially perturbing and updating control gains to improve performance. Access to flight measurements and the control gains is required, but no prior information about the aircraft dynamics or the flight control architecture is needed. After the procedure, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear flight dynamics simulation of a subscale airplane. Convergence was attained using five maneuvers that spanned approximately one minute of flight test time. Although shown for a relatively simple case, the method is general and can be applied to other aircraft, axes of motion, performance metrics, and control system designs.

Adaptive tuning↗
NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob↗
NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob J.↗
NASA NTRS1993

Synthesis of Optimal Nonlinear Feedback Laws for Dynamic Systems Using Neural Networks

Open-loop solutions of dynamical optimization problems can be numerically computed usingexisting software packages. The computed time histories of the state and control variables, formultiple sets of end conditions can then be used to train a neural network to 'recognize' the optimal,nonlinear feedback relation between the states and controls of the system. The 'learned' network canthen be used to output an approximate optimal control given a full set (or a partial set) of measuredsystem states. With simple neural networks, we have successfully demonstrated the efficacy of theproposed approach using a minimum-time orbit injection problem. The usefulness and limitations ofthis novel approach on real-life optimal guidance and control problems, with many state and control variables as well as path inequality constraints, remain to be seen.

Orbit injection problem↗
NASA NTRS2012

The Impact of Incentivizing the Use of Feedback on Learning and Performance in Educational Videogames

Educational videogames can be designed to provide instructional feedback responsive to specific actions. However, existing research indicates that students tend to ignore the feedback provided. That is, students often use ineffective help-seeking strategies. Research on the topic of help-seeking in learning environments have primarily focused on the role of cognitive factors, the nature of the help, or issues of timing and frequency. There is a noticeable gap in understanding how to motivate the use provided feedback. This study examined the relation between incentivizing the use of feedback and providing an explanation of the game's scoring rules on math learning in a pre-algebra videogame. A randomized-control design was used, comparing learning outcomes of students who received the incentive with those who did not. Results indicated that students given the incentive to use feedback had significantly higher normalized change scores on math items (d = .53), with stronger effects for students with low academic intrinsic motivation (d = .88 - 1.17).

Delacruz, Girlie C.↗
NASA NTRS1993

Attitude control of spacecraft using neural networks

This paper investigates the use of radial basis function neural networks for adaptive attitude control and momentum management of spacecraft. In the first part of the paper, neural networks are trained to learn from a family of open-loop optimal controls parameterized by the initial states and times-to-go. The trained is then used for closed-loop control. In the second part of the paper, neural networks are used for direct adaptive control in the presence of unmodeled effects and parameter uncertainty. The control and learning laws are derived using the method of Lyapunov.

Vadali, Srinivas R.↗
NASA NTRS1994

Intelligent Control Systems Research

Results of a three phase research program into intelligent control systems are presented. The first phase looked at implementing the lowest or direct level of a hierarchical control scheme using a reinforcement learning approach assuming no a priori information about the system under control. The second phase involved the design of an adaptive/optimizing level of the hierarchy and its interaction with the direct control level. The third and final phase of the research was aimed at combining the results of the previous phases with some a priori information about the controlled system.

Loparo, Kenneth A.↗
NASA NTRS2010

Hybrid Decompositional Verification for Discovering Failures in Adaptive Flight Control Systems

Adaptive flight control systems hold tremendous promise for maintaining the safety of a damaged aircraft and its passengers. However, most currently proposed adaptive control methodologies rely on online learning neural networks (OLNNs), which necessarily have the property that the controller is changing during the flight. These changes tend to be highly nonlinear, and difficult or impossible to analyze using standard techniques. In this paper, we approach the problem with a variant of compositional verification. The overall system is broken into components. Undesirable behavior is fed backwards through the system. Components which can be solved using formal methods techniques explicitly for the ranges of safe and unsafe input bounds are treated as white box components. The remaining black box components are analyzed with heuristic techniques that try to predict a range of component inputs that may lead to unsafe behavior. The composition of these component inputs throughout the system leads to overall system test vectors that may elucidate the undesirable behavior

Thompson, Sarah↗
NASA NTRS2016

Designing the Next Generation of Human Spacecraft

Lunar Space Station Common Module: A new concept for a module for a lunar space station attempts to reduce the module's mass by abandoning the traditional rack structure currently used on the ISS for the mounting of internal hardware and replacing it with a core structure. By using this design, the pressure shell will not have to carry the loads resulting from the internal mass. I worked with another intern to create the initial design for the module, with him focusing on the core and myself focusing on the pressure shell. To start, I was given the shell overall dimensions and material and tasked with sizing the wall thickness and placing stiffeners such that the shell could withstand the required loads. At the same time, I had to keep the mass to a minimum to keep the overall module within the allowable launch mass. Once I had done initial sizing based on pressure loads, I combined the pressure shell with the inner core to perform optimization of the design. Currently, the design involves circumferential stiffeners along the entire length of the pressure shell with longitudinal stiffeners on either end. In addition, extra wall thickness was added around each of the hatches. At this stage, the design shows a comparable mass to a more traditional design, but we are hopeful that, through optimization, we will be able to reduce the mass even further. There is currently a patent pending for the module design, for which I am listed as a co-inventor. ALON Material Testing: I was given samples of aluminum oxynitride (ALON) that had been impacted by a previous intern on which to perform residual strength tests as part of a plan to approve them for space use. Before testing, I measured the pucks and their damages using a ruler and optical micrometer in order to verify that the puck dimensions were within the tolerances set by the test guidelines and that the damages had not grown when the pucks were thinned. The test was a ring-ring test, which used two concentric rings to place the ring in axisymmetric bending, with the puck set up so that the damaged side was always in tension. Though I was unable to do the setup of the test or run the load machine due to a period of changing test procedures, I was able to observe the testing and perform the data collection. The pucks behaved as expected, breaking at the damage, as did the strengths calculated from the data, being lower than for the unimpacted pucks and having less scatter between the puck values. The attached image is of myself during the ALON strength testing. Over the course of my internship, I was able to learn much more about real-life structural analysis and about the behavior of materials, and it confirmed my previous interest in structural analysis. At the same time, due to the opportunities offered to interns, I was able to learn a lot about mission control, and, in doing so, I developed a second interest in working in mission control. In addition, being able to meet the people here and learn about the type of work NASA does made me want to come back to work for NASA full time.

Simmons, Emily↗
NASA NTRS1994

A neural network controller for automated composite manufacturing

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

Lichtenwalner, Peter F.↗
NASA NTRS2021

Safe Tracking Control of an Uncertain Euler-Lagrange System with Full-State Constraints using Barrier Functions

This paper presents a novel, safe tracking control design method that learns the parameters of an uncertain Euler-Lagrange (EL) system online using adaptive learning laws. A barrier function (BF) is first used to transform the full-state constrained EL-dynamics into an equivalent unconstrained dynamics. An adaptive tracking controller is then developed along with the parameter update law in the transformed state space such that the states remain bounded for all time within a prescribed bound. A stability analysis is developed that considers the EL-dynamics’ uncertainty, yielding a semi-globally uniformly ultimately bounded (SGUUB) tracking error and the parameter estimation error. The controller design is validated in simulations using a two-link planar manipulator. The results show the proposed method’s ability to track the reference trajectory while remaining inside each of the predefined state bounds.

Robots↗
NASA NTRS1984

The Effect of Part-simulation of Weightlessness on Human Control of Bilateral Teleoperation: Neuromotor Considerations

The effect of weightlessness on the human operator's performance in force reflecting position control of remote manipulators was investigated. A gravity compensation system was developed to simulate the effect of weightlessness on the operator's arm. A universal force reflecting hand controller (FRHC) and task simulation software were employed. Two experiments were performed because of anticipated disturbances in neuromotor control specification on the human operator in an orbital control environment to investigate: (1) the effect of controller stiffness on the attainment of a learned terminal position in the three dimensional controller space, and (2) the effect of controller stiffness and damping on force tracking of the contour of a simulated three dimensional cube using the part simulation of weightless conditions. The results support the extension of neuromotor control models, which postulate a stiffness balance encoding of terminal position, to three dimensional motion of a multilink system, confirm the existence of a disturbance in human manual control performance under gravity compensated conditions, and suggest techniques for compensation of weightlessness induced performance decrement through appropriate specification of hand controller response characteristics. These techniques are based on the human control model.

Corker, K.↗
NASA NTRS1990

Functional description of a command and control language tutor

The status of an ongoing project to explore the application of Intelligent Tutoring System (ITS) technology to NASA command and control languages is described. The primary objective of the current phase of the project is to develop a user interface for an ITS to assist NASA control center personnel in learning Systems Test and Operations Language (STOL). Although this ITS will be developed for Gamma Ray Observatory operators, it will be designed with sufficient flexibility so that its modules may serve as an ITS for other control languages such as the User Interface Language (UIL). The focus of this phase is to develop at least one other form of STOL representation to complement the operational STOL interface. Such an alternative representation would be adaptively employed during the tutoring session to facilitate the learning process. This is a key feature of this ITS which distinguishes it from a simulator that is only capable of representing the operational environment.

Elke, David R.↗
NASA NTRS2012

Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation

Traditional validation of flight control systems is based primarily upon empirical testing. Empirical testing is sufficient for simple systems in which a.) the behavior is approximately linear and b.) humans are in-the-loop and responsible for off-nominal flight regimes. A different possible concept of operation is to use adaptive flight control systems with online learning neural networks (OLNNs) in combination with a human pilot for off-nominal flight behavior (such as when a plane has been damaged). Validating these systems is difficult because the controller is changing during the flight in a nonlinear way, and because the pilot and the control system have the potential to co-adapt in adverse ways traditional empirical methods are unlikely to provide any guarantees in this case. Additionally, the time it takes to find unsafe regions within the flight envelope using empirical testing means that the time between adaptive controller design iterations is large. This paper describes a new concept for validating adaptive control systems using methods based on Bayesian statistics. This validation framework allows the analyst to build nonlinear models with modal behavior, and to have an uncertainty estimate for the difference between the behaviors of the model and system under test.

He, Yuning↗
NASA NTRS1994

Learning to train neural networks for real-world control problems

Over the past three years, our group has concentrated on the application of neural network methods to the training of controllers for real-world systems. This presentation describes our approach, surveys what we have found to be important, mentions some contributions to the field, and shows some representative results. Topics discussed include: (1) executing model studies as rehearsal for experimental studies; (2) the importance of correct derivatives; (3) effective training with second-order (DEKF) methods; (4) the efficacy of time-lagged recurrent networks; (5) liberation from the tyranny of the control cycle using asynchronous truncated backpropagation through time; and (6) multistream training for robustness. Results from model studies of automotive idle speed control serve as examples for several of these topics.

Feldkamp, Lee A.↗