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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.↗

NASA NTRS1979

Probable LAGEOS contributions to a worldwide geodynamics control network

The paper describes simulations performed on the contributions which LAGEOS laser ranging data can make to the establishment of a worldwide geodynamics control network. A distribution of 10 fixed ranging stations was assumed for most of the calculations, and a single 7-day arc was used, measurements assumed to be made every 10 minutes in order to avoid artificial reductions in the uncertainties due to oversampling. Computer simulations were carried out in which the coordinates of the stations and improvements in the gravity field coefficients were solved for simultaneously. It is suggested that good accuracy for station coordinates can be expected, even with the present gravity field model uncertainties, if sufficient measurement accuracy is achieved at a reasonable distribution of stations. Further, it is found that even 2-cm range measurement errors would be likely to be the main source of station coordinate errors in retrospective analyses of LAGEOS ranging results five or six years from now.

Bender, P. L.↗

NASA NTRS1997

Neural Network Control of a Magnetically Suspended Rotor System

Abstract Magnetic bearings offer significant advantages because of their noncontact operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. This paper discusses the use of a neural network as a nonlinear controller that circumvents system nonlinearity. A neural network controller was well trained and successfully demonstrated on a small magnetic bearing rig. This work demonstrated the feasibility of using a neural network to control nonlinear magnetic bearings and systems with unknown dynamics.

Choi, Benjamin↗

NASA NTRS1991

Neural-Network Control Of Prosthetic And Robotic Hands

Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

Buckley, Theresa M.↗

NASA NTRS1992

The rotation period, direction of the north pole, and geodetic control network of Venus

Three related activities that use Magellan data to derive improved estimates of the rotation period and direction of the spin axis of Venus are discussed. These are the computation of the Magellan geodetic control network, the use of measurement landmarks identified in overlapping image strips to improve the spacecraft ephemeris, and the use of common points identified on both the Venera 15/16 and the Magellan images. Since seven years separate the acquisition of the Magellan and Venera images, it should be possible to compute an accurate rotation period and possibly the spin vector of Venus.

Davies, M. E.↗