Search NASA⌕ Search

SEARCH · Search NASA

Results for “control of networks”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

NCC simulation model. Phase 2: Simulating the operations of the Network Control Center and NCC message manual

The network control center (NCC) provides scheduling, monitoring, and control of services to the NASA space network. The space network provides tracking and data acquisition services to many low-earth orbiting spacecraft. This report describes the second phase in the development of simulation models for the FCC. Phase one concentrated on the computer systems and interconnecting network.Phase two focuses on the implementation of the network message dialogs and the resources controlled by the NCC. Performance measures were developed along with selected indicators of the NCC's operational effectiveness.The NCC performance indicators were defined in terms of the following: (1) transfer rate, (2) network delay, (3) channel establishment time, (4) line turn around time, (5) availability, (6) reliability, (7) accuracy, (8) maintainability, and (9) security. An NCC internal and external message manual is appended to this report.

Benjamin, Norman M.↗

Robustness of a distributed neural network controller for locomotion in a hexapod robot

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

Chiel, Hillel J.↗

The control network of Mars: October 1986

The control network of Mars is composed of Mariner 9 frames which essentially give full coverage of the planet at low resolution. Superimposed on and tied to this network are strips of Viking mapping frames (resolution 100 to 250 m per pixel) which encircle the equator and 60 deg north latitude and multiple longitude ties between these latitude strips. There are multiple ties between these strips and the Viking 1 lander site. In the future another strip will be established at 60 deg south latitude. Because the Viking 1 lander site has been accurately located, the coordinates of points in its vicinity can be determined with an error of less than 100 m relative to an inertial coordinate system.

Davies, Merton E.↗

Phoebe: A preliminary control network and rotational elements

A preliminary control network for the Saturnian satellite Phoebe was determined based upon 6 distinct albedo features mapped on 16 Voyager 2 images. Using an existing map and an analytical triangulation program which minimized the measurement error, the north pole of Phoebe was calculated to be alpha sub 0 = 355.0 deg + or - 9.6 deg, delta sub 0 = 68.7 deg + or - 7.9 deg, where alpha sub 0, delta sub 0 are standard equatorial coordinates with equinox J2000 at epoch J2000. The prime meridian of Phoebe was computed to be W = 304.7 deg + 930.833872d, where d is the interval in days from JD 2451545.0 TDB.

Colvin, Tim R.↗

The control networks of the satellites of Jupiter

Geodetic control networks are being computed photogrammetrically for the large satellites of Jupiter, using pictures from the Voyager 1 and 2 encounters. Control points have been identified on the satellites and their coordinates computed by single-block analytical triangulation. The data sets have been converted from the B1950 to the J2000 inertial coordinate system to be compatible with future flight missions.

Davies, Merton E.↗

NCC Simulation Model: Simulating the operations of the network control center, phase 2

The simulation of the network control center (NCC) is in the second phase of development. This phase seeks to further develop the work performed in phase one. Phase one concentrated on the computer systems and interconnecting network. The focus of phase two will be the implementation of the network message dialogues and the resources controlled by the NCC. These resources are requested, initiated, monitored and analyzed via network messages. In the NCC network messages are presented in the form of packets that are routed across the network. These packets are generated, encoded, decoded and processed by the network host processors that generate and service the message traffic on the network that connects these hosts. As a result, the message traffic is used to characterize the work done by the NCC and the connected network. Phase one of the model development represented the NCC as a network of bi-directional single server queues and message generating sources. The generators represented the external segment processors. The served based queues represented the host processors. The NCC model consists of the internal and external processors which generate message traffic on the network that links these hosts. To fully realize the objective of phase two it is necessary to identify and model the processes in each internal processor. These processes live in the operating system of the internal host computers and handle tasks such as high speed message exchanging, ISN and NFE interface, event monitoring, network monitoring, and message logging. Inter process communication is achieved through the operating system facilities. The overall performance of the host is determined by its ability to service messages generated by both internal and external processors.

Benjamin, Norman M.↗

Phobos - Control network analysis

Initial analysis of the Mariner 9 high resolution pictures of Phobos surface features has been completed. A control network of 38 landmarks has been established and used to determine the physical size, shape, orientation, libration, and topography properties of Phobos. The results verified the synchronous rotation of Phobos and revealed a libration of approximately 5 deg in the orbit plane of Phobos. A preliminary map of Phobos, based on the control network analysis, is given.

Duxbury, T. C.↗

The Control Networks of the Satellites of Jupiter and Saturn

Geodetic control networks are being computed photogrammetrically for the large satellites of Jupiter and many of the satellites of Saturn using pictures from the Voyager 1 and 2 encounters. Control points have been identified on the satellites and their coordinates computed by single-block analytical triangulations. Contents of data sets for Io, Europa, Ganymede, Callisto, and Rhea are summarized.

Davies, M. E.↗

A network control concept for the 30/20 GHz communication system baseband processor

The architecture and system design for a satellite-switched TDMA communication system employing on-board processing was developed by Motorola for NASA's Lewis Research Center. The system design is based on distributed processing techniques that provide extreme flexibility in the selection of a network control protocol without impacting the satellite or ground terminal hardware. A network control concept that includes system synchronization and allows burst synchronization to occur within the system operational requirement is described. This concept integrates the tracking and control links with the communication links via the baseband processor, resulting in an autonomous system operational approach.

Sabourin, D. J.↗

The 1982 control network of Mars

Attention is given to a planet-wide control network of Mars that was computed in September 1982 using a large single-block analytical triangulation with 47,524 measurements of 6853 control points on 1054 Mariner 9 and 757 Viking pictures. In all, 19,139 normal equations were solved, with a resulting standard error of measurement of 18.06 microns. The control points identified by name and letter designation are given, as are the aerographic coordinates of the control points. In addition, the coordinates of the Viking I lander site are given: latitude, 22.480 deg; longitude, 47.962 deg (radius, 3389.32 km). This study expands and updates the previously published network (1978). It is noted that the computation differs in many respects from standard aerial mapping photogrammetric practice. In comparison with aerial mapping photography, the television formats are small and the focal lengths are long; stereo coverage is rare, the scale of the pictures varies greatly, and the residual camera distortions are large.

Davies, M. E.↗

Space Network Control Conference on Resource Allocation Concepts and Approaches

The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

Moe, Karen L.↗

The control networks of the satellites of Uranus

The present photogrammetric derivation of control networks for the five Uranus satellites Miranda, Ariel, Umbriel, Titania, and Oberon, on the basis of Voyager 2 images, covers the illuminated southern hemisphere of each satellite and respectively yields 103, 52, 43, 46, and 34 coordinate points. It is found that Titania is ellipsoidal, with 241-, 235-, and 232-km radii.In addition, mean radii are determined to be 579 km for Ariel, 586 km for Umbriel, 790 km for Titania, and 762 km for Oberon. Some of the control points used have been identified on the U.S. Geological Survey maps of these satellites.

Davies, Merton E.↗

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.↗

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.↗

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↗