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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 NTRS2018

Verification and Validation Challenges for Adaptive Flight Control of Complex Autonomous Systems

Autonomy of aerospace systems requires the ability for flight control systems to be able to adapt to complex uncertain dynamic environment. In spite of the five decades of research in adaptive control, the fact still remains that currently no adaptive control system has ever been deployed on any safety-critical or human-rated production systems such as passenger transport aircraft. The problem lies in the difficulty with the certification of adaptive control systems since existing certification methods cannot readily be used for nonlinear adaptive control systems. Research to address the notion of metrics for adaptive control began to appear in the recent years. These metrics, if accepted, could pave a path towards certification that would potentially lead to the adoption of adaptive control as a future control technology for safety-critical and human-rated production systems. Development of certifiable adaptive control systems represents a major challenge to overcome. Adaptive control systems with learning algorithms will never become part of the future unless it can be proven that they are highly safe and reliable. Rigorous methods for adaptive control software verification and validation must therefore be developed to ensure that adaptive control system software failures will not occur, to verify that the adaptive control system functions as required, to eliminate unintended functionality, and to demonstrate that certification requirements imposed by regulatory bodies such as the Federal Aviation Administration (FAA) can be satisfied. This presentation will discuss some of the technical issues with adaptive flight control and related V&V challenges.

Nguyen, Nhan T.↗
NASA NTRS1991

Intelligent control based on fuzzy logic and neural net theory

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

Lee, Chuen-Chien↗
NASA NTRS1988

Pitch-Learning Algorithm For Speech Encoders

Adaptive algorithm detects and corrects errors in sequence of estimates of pitch period of speech. Algorithm operates in conjunction with techniques used to estimate pitch period. Used in such parametric and hybrid speech coders as linear predictive coders and adaptive predictive coders.

Bhaskar, B. R. Udaya↗
NASA NTRS1993

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

Padgett, Mary L.↗
NASA NTRS1997

Automating the Process of Optimization in Spacecraft Design

Spacecraft design optimization is a difficult problem, due to the complexity of optimization cost surfaces, and human expertise in optimization that is necessary in order to achieve good results. In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms (e.g., generic algorithms, simulated annealing), which is configured for a particular optimization problem by an adaptive problem solver based on artificial intelligence and machine learning techniques. We describe work in progress on OASIS, a system for adaptive problem solving based on these principles.

optimization↗
NASA NTRS1999

Visual-vestibular integration motion perception reporting

Self-orientation and self/surround-motion perception derive from a multimodal sensory process that integrates information from the eyes, vestibular apparatus, proprioceptive and somatosensory receptors. Results from short and long duration spaceflight investigations indicate that: (1) perceptual and sensorimotor function was disrupted during the initial exposure to microgravity and gradually improved over hours to days (individuals adapt), (2) the presence and/or absence of information from different sensory modalities differentially affected the perception of orientation, self-motion and surround-motion, (3) perceptual and sensorimotor function was initially disrupted upon return to Earth-normal gravity and gradually recovered to preflight levels (individuals readapt), and (4) the longer the exposure to microgravity, the more complete the adaptation, the more profound the postflight disturbances, and the longer the recovery period to preflight levels. While much has been learned about perceptual and sensorimotor reactions and adaptation to microgravity, there is much remaining to be learned about the mechanisms underlying the adaptive changes, and about how intersensory interactions affect perceptual and sensorimotor function during voluntary movements. During space flight, SMS and perceptual disturbances have led to reductions in performance efficiency and sense of well-being. During entry and immediately after landing, such disturbances could have a serious impact on the ability of the commander to land the Orbiter and on the ability of all crew members to egress from the Orbiter, particularly in a non-nominal condition or following extended stays in microgravity. An understanding of spatial orientation and motion perception is essential for developing countermeasures for Space Motion Sickness (SMS) and perceptual disturbances during spaceflight and upon return to Earth. Countermeasures for optimal performance in flight and a successful return to Earth require the development of preflight and in-flight training to help astronauts acquire and maintain a dual adaptive state. Despite the considerable experience with, and use of, an extensive set of countermeasures in the Russian space program, SMS and perceptual disturbances remain an unresolved problem on long-term flights. Reliable, valid perceptual reports are required to develop and refine stimulus rearrangements presented in the PAT devices currently being developed as countermeasures for the prevention of motion sickness and perceptual disturbances during spaceflight, and to ensure a less hazardous return to Earth. Prior to STS-8, crew member descriptions of their perceptual experiences were, at best, anecdotal. Crew members were not schooled in the physiology or psychology of sensory perception, nor were they exposed to the appropriate professional vocabulary. However, beginning with the STS-8 Shuttle flight, a serious effort was initiated to teach astronauts a systematic method to classify and quantify their perceptual responses in space, during entry, and after flight. Understanding, categorizing, and characterizing perceptual responses to spaceflight has been greatly enhanced by implementation of that training system.

Harm, Deborah L.↗
NASA NTRS2006

Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

Jacklin, Stephen A.↗
NASA NTRS2020

Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning

This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.

Viros-i-Martin, Antoni↗
NASA NTRS1981

A case study of the influences of audience and purpose on the composing processes of an engineer

The design and preliminary findings of a study of composing processes (on the job) of engineers, managers, and scientists is presented. The influences of audience and purpose on the composing process of engineers was of concern; specifically, the cognitive processes, physical behaviors, and factors that influence the evoluton of a piece of writing. An overview of the study, related literature, outlines of research design, and preliminary findings from a case study of engineers are given. It is suggested that teaching be adapted to help students learn to represent rhetorical problems to guide composing for effective writing.

Stalnaker, B. J.↗
NASA NTRS1989

An improved adaptive control for repetitive motion of robots

An adaptive control algorithm is proposed for a class of nonlinear systems, such as robotic manipulators, which is capable of improving its performance in repetitive motions. When the task is repeated, the error between the desired trajectory and that of the system is guaranteed to decrease. The design is based on the combination of a direct adaptive control and a learning process. This method does not require any knowledge of the dynamic parameters of the system.

Pourboghrat, F.↗
NASA NTRS2003

Cost savings through multimission code reuse for Mars image products

An overview of the library's design will be presented, along with mission adaptation experiences and lessons learned, and the kinds of additional functionality that have been added while still retaining its multimission character. The application programs using the library will also be briefly described.

cost savings ground system image processing Mars i↗
NASA NTRS2014

Engineering Antifragile Systems: A Change In Design Philosophy

While technology has made astounding advances in the last century, problems are confronting the engineering community that must be solved. Cost and schedule of producing large systems are increasing at an unsustainable rate and these systems often do not perform as intended. New systems are required that may not be achieved by current methods. To solve these problems, NASA is working to infuse concepts from Complexity Science into the engineering process. Some of these problems may be solved by a change in design philosophy. Instead of designing systems to meet known requirements that will always lead to fragile systems at some degree, systems should be designed wherever possible to be antifragile: designing cognitive cyberphysical systems that can learn from their experience, adapt to unforeseen events they face in their environment, and grow stronger in the face of adversity. Several examples are presented of on ongoing research efforts to employ this philosophy.

Jones, Kennie H.↗
NASA NTRS2014

UAS Applications for Hurricane Science, Hurrican and Severe Storm Sentinel (HS3)

Earth Science Industry Update: UAS Applications for Hurricane Science Unmanned systems can significantly transform hurricane observations and monitoring, improving our knowledge about and ability to forecast storm formation, track, and intensity change. NASA's use of the Global Hawk has demonstrated the scientific value of this platform and provided a proof-of-concept for operational applications. However, science flight operations face several challenges and constraints. In this session, learn about how NASA adapted the Global Hawk to do science; How NASA conducts its hurricane missions, and some of the challenges and constraints they face; Science results from NASA's recent hurricane field campaigns using the Global Hawk. How assimilation of dropsonde and radar data into weather prediction models may improve forecast accuracy; Other Earth science problems that could be addressed with Global Hawks.

Hurricane Science↗
NASA NTRS2022

Run Time Assurance for Electric Vertical Takeoff and Landing Aircraft

NASA is conducting research to demonstrate and evaluate the application of Run Time Assurance (RTA) as a means to assure safety in Electric Vertical Takeoff and Landing (eVTOL) aircraft with highly automated or autonomous flight capability supervised by a single onboard pilot. The work described in this report demonstrates an application of RTA and examines the implications for design and analysis of aircraft functions and systems; aircraft safety hazards; safety assurance; development assurance; and pilot tasks and performance. This research effort also seeks to assess the efficacy of the combined application of traditional Functional Hazard Analysis (FHA) and the more modern System Theoretic Process Analysis (STPA) techniques to perform hazard analyses on aircraft with complex automated and autonomous systems and an onboard pilot. During the research effort we developed architectural designs of two alternate eVTOL aircraft, generally following the process characterized in the SAE standards ARP4754 and ARP4761. The design has focused on the control architectures of these aircraft, which are identical except that one incorporates RTA techniques to reduce the criticality of some key software components. Artifacts of this process include a taxonomy of aircraft-level functions, aircraft-level architecture diagrams, aircraft-level functional hazard assessments (AFHA), function allocations onto aircraft systems and subsystems, functional block diagrams for a select set of control-related functions, and system-level functional hazard assessments (SFHA) for those functions. This project has highlighted the notion that DAL D is something of a sweet spot for low-confidence controllers in an RTA-based design. Among the many activities described in DO-178C, the activities related to requirement verifiability, algorithmic accuracy, and test coverage can be the most challenging for the kinds of advanced control techniques that may be desirable in novel UAM designs, such as adaptive control, machine-learning, artificial intelligence, numerical search, and Monte Carlo based algorithms. Moreover, the standard requires that development teams demonstrate that errors leading to unacceptable failure conditions have been removed from the software. The RTA architecture, which cordons off the low-confidence function, makes it much easier to show this for these kinds of algorithms. With regard to the use of STPA and FHA as complementary hazard analysis techniques, our research effort led us to the conclusion that STPA should be used to derive requirements for hardware and software systems and/or components. Also, STPA is a natural complement to other processes in ARP4754A involving design studies and iteration.

Run-time assurance↗
NASA NTRS2020

A Reinforcement Learning Framework for Space Missions in Unknown Environments

A land-and-traverse mission to icy worlds such as Europa and Enceladus is challenging due to lack of prior knowledge regarding the terrain conditions. Previous work [1] showed that rovers with high degrees of freedom (DoF) can achieve robust traversal by leveraging redundant modes for mobility to counter terrain uncertainty (e.g. walking, driving, or inch-worming). This paper presents a generic and scalable reinforcement learning scheme for enabling on-board decision making on rovers to automatically switch between modes of traversal based on online performance feedback. The objective is to maximize energy efficiency, minimize operator input and successfully negotiate unstructured terrain conditions without relying on exhaustive prior knowledge. The proposed methodology is well grounded in the literature on reinforcement learning and has been adapted to address conformance to validation and verification requirements and JPL flight operations history of using per-sol prescribed sequences for a space mission.

Tavallali, Peyman↗
NASA NTRS2019

Deep Learning and Uncertainty Quantification for Climate Resilience

Modeling and monitoring of earth’s processes through physical models and satellite observations at high resolutions is crucial for ensuring society’s ability to adapt to climate change. Deep learning (DL) has been shown to be a valuable tool for generating high resolution data, emulating physical models, and detecting weather patterns which can then be used to inform stakeholders and decision makers. However, both the data and model parameters contain substantial uncertainties that may alter users’ decisions. In this work we present two DL applications on high-resolution climate and satellite datasets using Bayesian neural networks to generate well calibrated uncertainty estimates.

Vandal, Thomas↗
NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗