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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
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 NTRS1990

Development of a sensor coordinated kinematic model for neural network controller training

A robotic benchmark problem useful for evaluating alternative neural network controllers is presented. Specifically, it derives two camera models and the kinematic equations of a multiple degree of freedom manipulator whose end effector is under observation. The mapping developed include forward and inverse translations from binocular images to 3-D target position and the inverse kinematics of mapping point positions into manipulator commands in joint space. Implementation is detailed for a three degree of freedom manipulator with one revolute joint at the base and two prismatic joints on the arms. The example is restricted to operate within a unit cube with arm links of 0.6 and 0.4 units respectively. The development is presented in the context of more complex simulations and a logical path for extension of the benchmark to higher degree of freedom manipulators is presented.

Jorgensen, Charles C.↗
NASA NTRS2016

Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure, and indeed security solutions are being implemented in the current system. While these security solutions are important, they only provide a piecemeal solution. Particular computers or communication channels are protected from particular attacks, without a holistic view of the air transportation infrastructure. On the other hand, the above-listed incidents highlight that a holistic approach is needed, for several reasons. First, the air transportation infrastructure is a large scale cyber-physical system with multiple stakeholders and diverse legacy assets. It is impractical to protect every cyber- asset from known and unknown disruptions, and instead a strategic view of security is needed. Second, disruptions to the cyber- system can incur complex propagative impacts across the air transportation network, including its physical and human assets. Also, these implications of cyber- events are exacerbated or modulated by other disruptions and operational specifics, e.g. severe weather, operator fatigue or error, etc. These characteristics motivate a holistic and strategic perspective on protecting the air transportation infrastructure from cyber- events. The analysis of cyber- threats to the air traffic system is also inextricably tied to the integration of new autonomy into the airspace. The replacement of human operators with cyber functions leaves the network open to new cyber threats, which must be modeled and managed. Paradoxically, the mitigation of cyber events in the airspace will also likely require additional autonomy, given the fast time scale and myriad pathways of cyber-attacks which must be managed. The assessment of new vulnerabilities upon integration of new autonomy is also a key motivation for a holistic perspective on cyber threats.

Complex Networks↗
NASA NTRS1976

Selenodetic control network

Details for identifying the first 66 reference points of a selenodetic control network are presented. These reference points consist of craters having diameters larger than 8 km and which are therefore detectable on most plates used so far in lunar photogrammetric studies. The distribution of the adopted 211 reference points on Blagg-Mueller (1935) maps is given along with the Blagg-Mueller catalog number of each crater. The correspondence is indicated among the Blagg-Mueller number, the Lunar and Planetary Laboratory Catalog number, and the frame number of Lunar Orbiter IV photographs on which the craters have been studied.

Moutsoulas, M.↗
NASA NTRS1991

A unified lunar control network: April 1991

This program was designed to combine and transform various control networks of the Moon into a common center-of-mass coordinate system. The first phase, dealing with the near side, was completed and published. This report contains coordinates of 1166 points on the near side of the Moon.

Davies, Merton E.↗
NASA NTRS1996

A global spacecraft control network for spacecraft autonomy research

The development and implementation of the Automated Space System Experimental Testbed (ASSET) space operations and control network, is reported on. This network will serve as a command and control architecture for spacecraft operations and will offer a real testbed for the application and validation of advanced autonomous spacecraft operations strategies. The proposed network will initially consist of globally distributed amateur radio ground stations at locations throughout North America and Europe. These stations will be linked via Internet to various control centers. The Stanford (CA) control center will be capable of human and computer based decision making for the coordination of user experiments, resource scheduling and fault management. The project's system architecture is described together with its proposed use as a command and control system, its value as a testbed for spacecraft autonomy research, and its current implementation.

Kitts, Christopher A.↗
NASA NTRS1989

Phobos and Deimos control networks

Viking Orbiter images of Phobos and Deimos have been measured to establish global control networks for 98 surface features of the former and 53 of the latter; photogrammetric triangulation has yielded body-fixed coordinates of these control-points, as well as mean triaxial radii of 13.3 x 11.1 x 9.3 km for Phobos and 7.5 x 6.2 x 5.4 for Deimos. Expressions are also obtained for the inertial orientations of these bodies' spin axes and prime meridians. While these expressions should be accurate to a few tenths of a deg for the 1971-1980 period, their accuracy will degrade with time as the orbit accuracy degrades.

Duxbury, Thomas C.↗
NASA NTRS2001

A Closed-Loop Optimal Neural-Network Controller to Optimize Rotorcraft Aeromechanical Behaviour: Output from Two Sample Cases - Volume 2

A closed-loop optimal neural-network controller technique was developed to optimize rotorcraft aeromechanical behaviour. This technique utilities a neural-network scheme to provide a general non-linear model of the rotorcraft. A modem constrained optimisation method is used to determine and update the constants in the neural-network plant model as well as to determine the optimal control vector. Current data is read, weighted, and added to a sliding data window. When the specified maximum number of data sets allowed in the data window is exceeded, the oldest data set is and the remaining data sets are re-weighted. This procedure provides at least four additional degrees-of-freedom in addition to the size and geometry of the neural-network itself with which to optimize the overall operation of the controller. These additional degrees-of-freedom are: 1. the maximum length of the sliding data window, 2. the frequency of neural-network updates, 3. the weighting of the individual data sets within the sliding window, and 4. the maximum number of optimisation iterations used for the neural-network updates.

Leyland, Jane Anne↗
NASA NTRS1991

Publication of topographic atlas and control network of Mars

To aid planetary studies and the planning of future Mars missions, the Topographic Atlas and Control Network for Mars will be submitted by the end of fiscal year 1992 for publication as a NASA Special Publication. It will consist of reduced versions of 108 1:2 million-scale photomosaics that show contour lines from topographic maps at the same scale, as well as precisely located control points. The control points are from the planetwide network, which is not only instrumental in the compilation of maps at various scales, but is also widely used in other research such as studies of Mars' gravity and atmosphere. An example, a combination of MC 8-NW and -SW, of the photomosaics to be included in the atlas is presented. Contour lines in the figure are at 1-km intervals. The final adjusted ground coordinates and elevations of the 77 control points shown are given in table form. The last column in the table lists the topographic datum (zero elevation) that can be used to compute the solid radius of the control point from the center of mass of Mars. The atlas will also include information such as the adjusted C-matrices of each image, descriptions of the methods used, and their accuracy, and guidelines for users.

Wu, Sherman S. C.↗
NASA NTRS1993

Training augmentation device for the Air Force satellite Control Network

From the 1960's and into the early 1980's satellite operations and control were conducted by Air Force Systems Command (AFSC), now Air Force Materiel Command (AFMC), out of the Satellite Control Facility at Onizuka AFB, CA. AFSC was responsible for acquiring satellite command and control systems and conducting routine satellite operations. The daily operations, consisting of satellite health and status contacts and station keeping activities, were performed for AFSC by a Mission Control Team (MCT) staffed by civilian contractors who were responsible for providing their own technically 'qualified' personnel as satellite operators. An MCT consists of five positions: mission planner, ground controller, planner analyst, orbit analyst, and ranger controller. Most of the training consisted of On-the-Job-Training (OJT) with junior personnel apprenticed to senior personnel until they could demonstrate job proficiency. With most of the satellite operators having 15 to 25 years of experience, there was minimal risk to the mission. In the mid 1980's Air Force Space Command (AFSPACOM) assumed operational responsibility for a newly established control node at Falcon AFB (FAFB) in CO. The satellites and ground system program offices (SPO's) are organized under AFSC's Space and Missiles Systems Center (SMC) to function as a systems engineering and acquisition agency for AFSPACECOM. The collection of the satellite control nodes, ground tracking stations, computer processing equipment, and connecting communications links is referred to as the Air Force Satellite Control Network (AFSCN).

Shoates, Keith B.↗
NASA NTRS1983

The control networks of Mimas and Enceladus

A bundle-type analytical triangulation program is employed to compute control networks for Mimas, whose network encircles the satellite with 110 points measured on 32 Voyager 1 pictures, and Enceladus, whose network does not completely encircle the satellite and contains 71 points measured on 22 Voyager 2 pictures. Many of the control points are identified on illustrations and by name, and their coordinates are presented in tabular form. The analytical triangulation program was used to solve for the mean radii and three principal axes of best-fit ellipsoids. The mean radius of Mimas is 197 + or - 3 km, while that of Enceladus is 251 + or - 5 km.

Davies, M. E.↗
NASA NTRS1985

The Control Network of Mars: October 1984

Strips of Viking mapping pictures are being added to the planetwide control networks of Mars. These high resolution strips run from the Viking 1 lander site east to Airy-O, north along the 0 deg meridian to 60 deg latitude, southwest through the Viking 1 lander site to the equator, and along the equator encircling the planet. Everywhere along these strips, old points are incorporated in the measurements, thus assuring that the strips and planetwide net make a single large data set. The control points are much denser in the areas covered by the strips than in those regions not covered by strips and their coordinates of the control points is estimated to be less than 3 km and the error in longitude of a few points near Airy-O is less than 40 m. The horizontal coordinates of the control points on Mars have been updated with a single-block planetwide analytical triangulation. The standard error of measurement was 18.06 micron m. The longitude of the Viking 1 lander site was 47 deg.962 and the latitude 22 deg. 480. The latitude of Airy-O was -5 deg. 152.

Davies, M. E.↗
NASA NTRS1995

Neural-Network Controller For Vibration Suppression

Neural-network-based adaptive-control system proposed for vibration suppression of flexible space structures. Controller features three-layer neural network and utilizes output feedback. Measurements generated by various sensors on structure. Feed forward path also included to speed up response in case plant exhibits predominantly linear dynamic behavior. System applicable to single-input single-output systems. Work extended to multiple-input multiple-output systems as well.

Boussalis, Dhemetrios↗
NASA NTRS1995

Experiments in Neural-Network Control of a Free-Flying Space Robot

Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

NEURAL NETS↗
NASA NTRS1991

Space Network Control (SNC) Conference on Resource Allocation Concepts and Approaches. Overview

In session 1 of the conference, Concepts for space network resource allocation was the main topic. In session 2, Space Network Control and user payload operations and control center human-computer interface, was the topic of discussion. The topic of session 3 was Resource allocation tools, technology, and algorithms. Some of the stated goals for the conference are as follows: to survey existing resource allocation concepts and approaches; to identify solutions applicable to the SN problem; to identify fruitful avenues of study in support of SNC development; and to capture knowledge in proceedings and make available to bidders on the SNC concept definition procurement.

Source record↗
NASA NTRS1995

Dissipative rendering and neural network control system design

Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using their universal approximation property, neural networks have been introduced into nonlinear control designs in several ways. Unfortunately, little work has appeared that analyzes neural network control systems and establishes margins for stability and performance. One approach for this analysis is to set up neural network control systems in the framework presented above. For example, one neural network could be used to render a system to be dissipative, a second strictly dissipative neural network controller could be used to guarantee robust stability.

Gonzalez, Oscar R.↗
NASA NTRS2002

A MOLA-controlled RAND-USGS Control Network for Mars

We are undertaking, in support of the Mars Digital Image Mosaic (MDIM) 2.1, many improvements in the RAND-USGS photogrammetric control network for Mars, primarily involving the use of Mars Orbiter Laser Altimeter (MOLA)-derived radii and DIMs to improve control point absolute radii and horizontal positions. Additional information is contained in the original extended abstract.

Archinal, B. A.↗
DOE OSTI2023

Virtualizing Industrial Control Networks for Cyber Resilience Experiments

Industrial control systems (ICS) networks are undergoing constant shifts to accommodate new security measures. It is challenging to test varying network configurations and security tools with physical systems as they typically include large, expensive equipment. Not only this, but researchers often do not have access to this type of equipment for development of new security tools and techniques. As a solution to these issues, this work presents a set of tools for utilizing GNS3 and Docker as a virtual ICS network. Additionally, the virtual network can be attached to physical devices including network switches, hardware simulations, and intelligent electronic devices (IEDs). Two case studies showcase a relatively complex automatically generated network and an attack on a simple ICS network with an example mitigation.

42 ENGINEERING↗