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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Control of Complex Dynamic Systems by Neural Networks

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Spall, James C.↗
NASA NTRS2004

Pilots Rate Augmented Generalized Predictive Control for Reconfiguration

The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.

Soloway, Don↗
NASA NTRS2020

Dynamic Stability And Adaptive Control of Networked Evolving Formations with Weak Nonlinearities

The dynamic stability of formation geometry is vital to the design of large scale multiagent systems. In this paper, we probe into the structure of the formation system matrix using the Laplacian of a digraph to develop several fundamental theoretical results on the stability of formation geometry. Our key-results include the integration of the graph Laplacian into linear and weak-nonlinear relative dynamics, the development of several new coordinate transformations that expose the influence of the graph Laplacian matrix on the control laws of the agents, and the use of direct adaptive control as stability restoring devices. We also develop two fundamental results that provide upper bounds for stable formation evolution under nonlinear perturbations of agent dynamics. Finally, we use an illustrative example to demonstrate our theoretical findings.

Gehlot, Vinod P.↗
NASA NTRS2011

Intelligent Wireless Sensor Networks for System Health Monitoring

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of PAN configuration, providing the appropriate response for maintaining overall sensor system function, even when sensor modules fail or the WSN is reconfigured. The session will present the architecture and technical feasibility of creating fault-tolerant WSNs for aerospace applications based on our application of the technology to a Structural Health Monitoring testbed. The interim results of WSN development and testing including our software architecture for intelligent sensor management will be discussed in the context of the specific tradeoffs required for effective use. Initial certification measurement techniques and test results gauging WSN susceptibility to Radio Frequency interference are introduced as key challenges for technology adoption. A candidate Developmental and Flight Instrumentation implementation using intelligent sensor networks for wind tunnel and flight tests is developed as a guide to understanding key aspects of the aerospace vehicle design, test and operations life cycle.

networks↗
NASA NTRS1993

On neural networks in identification and control of dynamic systems

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

Phan, Minh↗
NASA NTRS1990

Weakly connected neural nets

A new neural network architecture is proposed based upon effects of non-Lipschitzian dynamics. The network is fully connected, but these connections are active only during vanishingly short time periods. The advantages of this architecture are discussed.

Zak, Michail↗
NASA NTRS1990

Weakly connected neural nets

A new neural network architecture is proposed based upon effects of non-Lipschitzian dynamics. The network is fully connected, but these connections are active only during vanishingly short time periods. The advantages of this architecture are discussed.

Zak, Michail↗
NASA NTRS1997

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Maghami, Peiman G.↗
NASA NTRS2005

Temporal Dynamic Controllability Revisited

An important issue for temporal planners is the ability to handle temporal uncertainty. We revisit the question of how to determine whether a given set of temporal requirements are feasible in the light of uncertain durations of some processes. In particular, we consider how best to determine whether a network is Dynamically Controllable, i.e., whether a dynamic strategy exists for executing the network that is guaranteed to satisfy the requirements. Previous work has shown the existence of a pseudo-polynomial algorithm for testing Dynamic Controllability. Here, we greatly simplify the previous framework, and present a true polynomial algorithm with a cutoff based only on the number of nodes.

Morris, Paul H.↗
NASA NTRS2017

Mesh Network Architecture for Enabling Inter-Spacecraft Communication

To enable communication between spacecraft operating in a formation or small constellation, a mesh network architecture was developed and tested using a time division multiple access (TDMA) communication scheme. The network is designed to allow for the exchange of telemetry and other data between spacecraft to enable collaboration between small spacecraft. The system uses a peer-to-peer topology with no central router, so that it does not have a single point of failure. The mesh network is dynamically configurable to allow for addition and subtraction of new spacecraft into the communication network. Flight testing was performed using an unmanned aerial system (UAS) formation acting as a spacecraft analogue and providing a stressing environment to prove mesh network performance. The mesh network was primarily devised to provide low latency, high frequency communication but is flexible and can also be configured to provide higher bandwidth for applications desiring high data throughput. The network includes a relay functionality that extends the maximum range between spacecraft in the network by relaying data from node to node. The mesh network control is implemented completely in software making it hardware agnostic, thereby allowing it to function with a wide variety of existing radios and computing platforms..

Becker, Christopher↗
NASA NTRS2008

Dynamic Routing for Delay-Tolerant Networking in Space Flight Operations

Contact Graph Routing (CGR) is a dynamic routing system that computes routes through a time-varying topology composed of scheduled, bounded communication contacts in a network built on the Delay-Tolerant Networking (DTN) architecture. It is designed to support operations in a space network based on DTN, but it also could be used in terrestrial applications where operation according to a predefined schedule is preferable to opportunistic communication, as in a low-power sensor network. This paper will describe the operation of the CGR system and explain how it can enable data delivery over scheduled transmission opportunities, fully utilizing the available transmission capacity, without knowing the current state of any bundle protocol node (other than the local node itself) and without exhausting processing resources at any bundle router.

CGR↗
NASA NTRS2009

Efficient File Sharing by Multicast - P2P Protocol Using Network Coding and Rank Based Peer Selection

In this work, we consider information dissemination and sharing in a distributed peer-to-peer (P2P highly dynamic communication network. In particular, we explore a network coding technique for transmission and a rank based peer selection method for network formation. The combined approach has been shown to improve information sharing and delivery to all users when considering the challenges imposed by the space network environments.

military↗
NASA NTRS2014

Dynamic Controllability and Dispatchability Relationships

An important issue for temporal planners is the ability to handle temporal uncertainty. Recent papers have addressed the question of how to tell whether a temporal network is Dynamically Controllable, i.e., whether the temporal requirements are feasible in the light of uncertain durations of some processes. We present a fast algorithm for Dynamic Controllability. We also note a correspondence between the reduction steps in the algorithm and the operations involved in converting the projections to dispatchable form. This has implications for the complexity for sparse networks.

controllability↗
NASA NTRS2006

A Structural Characterization of Temporal Dynamic Controllability

An important issue for temporal planners is the ability to handle temporal uncertainty. Recent papers have addressed the question of how to tell whether a temporal network is Dynamically Controllable, i.e., whether the temporal requirements are feasible in the light of uncertain durations of some processes. Previous work has presented an O(N5) algorithm for testing this property. Here, we introduce a new analysis of temporal cycles that leads to an O(N4) algorithm.

Morris, Paul↗
NASA NTRS1994

Optimal Space Station solar array gimbal angle determination via radial basis function neural networks

The potential for excessive plume impingement loads on Space Station Freedom solar arrays, caused by jet firings from an approaching Space Shuttle, is addressed. An artificial neural network is designed to determine commanded solar array beta gimbal angle for minimum plume loads. The commanded angle would be determined dynamically. The network design proposed involves radial basis functions as activation functions. Design, development, and simulation of this network design are discussed.

Clancy, Daniel J.↗
NASA NTRS2020

Dynamic Medical Risk Assessment Supported by Inference Networks

The Human Research Program's next generation risk model, the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT), provides researchers with estimates of astronaut medical and health risk. MEDPRATs Susceptibility Inference Network (SIN) facilitates the dynamic component of this tool. The SIN provides MEDPRAT with a "memory" that allows the system to use knowledge of what simulation events have occurred to alter the representative probability that future simulation events will occur during a given trial. The SIN allows for the medical events being simulated to be related to, and influence the likelihood of one another, providing a more robust risk estimate. We present initial work of our efforts to mathematically quantify and represent these dependent relationships between medical events.

McIntyre, L.↗
NASA NTRS1983

Meteorological and dynamical requirements for MST radar networks: Waves

Studies of wave motions using the MST radar have concentrated on single station time series analyses of gravity waves and tides. Since these radars collect high time resolution data they have the potential to become a significant tool for mesoscale research. In addition, radars are operated almost continuously unattended and, consequently, data sets are available for analyzing longer period wave motions such as tides and planetary scale waves. Although there is much to learn from single station data, the possibilities of new knowledge from a network of radars is exciting. The scales of wave motions in the atmosphere cover a broad range. Consequently the choice of a radar network depends to a large extent on the types of wave motions that are studied. There are many outstanding research problems that would benefit from observations from a MST radar network. In particular, there is a strong need for measurements of gravity wave parameters and equatorial wave motions. Some of the current problems in wave dynamics are discussed.

Avery, S. K.↗