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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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The CLAS12 Data Acquisition System was designed and built as part of the CLAS12 detector project in Hall B at Jefferson Laboratory. This article contains a full description of the system, including requirements, design, hardware, and software descriptions, as well as the achieved performance. The associated computing, network, and slow controls systems are also described.
Quantum networks are needed for quantum domain applications that may run across different network deployments - point-to-point, multi-node networks, and possibly across inter-domain quantum networks, like the quantum internet. Unlike classical computing networks, quantum networks involve heterogeneous systems nodes that currently require local and manual control. This needs a unified control approach to help them integrate, work seamlessly, and have global knowledge. Software-defined Networking (SDN) has been successfully leveraged in classical networking for seamless and software-driven control of network infrastructure and packet switching, but not in management of quantum network applications across heterogeneous systems. In this paper, we review the current state of the art and present early developments of a System-of-Systems Software-defined Quantum Networking architecture (SoS-SDQN), a generic architecture that supports software-driven quantum network experiments across heterogeneous quantum systems. The architecture modifies the generic SDN to address the domain requirements of quantum networks and proposes a multilevel SDQN to provide a global view of network status, running applications, and control of incorporated heterogeneous quantum systems. We also design and implement a SoS SouthBound Quantum Interface (SoS-SBQI), a quantum infrastructure protocol that abstracts automation of applications across the network.
NASA's vision for an intelligent engine will be realized with the development of a truly distributed control system featuring highly reliable, modular, and dependable components capable of both surviving the harsh engine operating environment and decentralized functionality. A set of control system verification tools was developed and applied to a C-MAPSS40K engine model, and metrics were established to assess the stability and performance of these control systems on the same platform. A software tool was developed that allows designers to assemble easily a distributed control system in software and immediately assess the overall impacts of the system on the target (simulated) platform, allowing control system designers to converge rapidly on acceptable architectures with consideration to all required hardware elements. The software developed in this program will be installed on a distributed hardware-in-the-loop (DHIL) simulation tool to assist NASA and the Distributed Engine Control Working Group (DECWG) in integrating DCS (distributed engine control systems) components onto existing and next-generation engines.The distributed engine control simulator blockset for MATLAB/Simulink and hardware simulator provides the capability to simulate virtual subcomponents, as well as swap actual subcomponents for hardware-in-the-loop (HIL) analysis. Subcomponents can be the communication network, smart sensor or actuator nodes, or a centralized control system. The distributed engine control blockset for MATLAB/Simulink is a software development tool. The software includes an engine simulation, a communication network simulation, control algorithms, and analysis algorithms set up in a modular environment for rapid simulation of different network architectures; the hardware consists of an embedded device running parts of the CMAPSS engine simulator and controlled through Simulink. The distributed engine control simulation, evaluation, and analysis technology provides unique capabilities to study the effects of a given change to the control system in the context of the distributed paradigm. The simulation tool can support treatment of all components within the control system, both virtual and real; these include communication data network, smart sensor and actuator nodes, centralized control system (FADEC full authority digital engine control), and the aircraft engine itself. The DECsim tool can allow simulation-based prototyping of control laws, control architectures, and decentralization strategies before hardware is integrated into the system. With the configuration specified, the simulator allows a variety of key factors to be systematically assessed. Such factors include control system performance, reliability, weight, and bandwidth utilization.
Console Computer Interface Adapter (CCIA) to provide communication between flight control consoles and telemetry and command computers of Apollo network
A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.
The facilities, programming system, and monitor and control system for the deep space network are described. Ongoing planetary and interplanetary flight projects are reviewed, along with tracking and ground-based navigation, communications, and network and facility engineering.
Adaptive/general learning algorithms using varying neural network models are considered for the intelligent control of robotic arm plus dextrous hand/manipulator systems. Results are summarized and discussed for the use of the Barto/Sutton/Anderson neuronlike, unsupervised learning controller as applied to the stabilization of an inverted pendulum on a cart system. Recommendations are made for the application of the controller and a kinematic analysis for trajectory planning to simple object retrieval (chase/approach and capture/grasp) scenarios in two dimensions.
Operational Technology (OT) networks [e.g., industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems] have unique cyber security challenges due to their decades long service life, high availability requirements, and limited visibility. OT networks often take credit for being “air gapped” (i.e. disconnected from the Internet) and all devices within the OT network can “talk” to each other—even if they should not. This SPaRC Technical Bulletin describes how the unique limitations of OT networks can become strengths when it comes to cybersecurity.
This research is designed to apply a new information processing technology, artificial neural networks, to monitoring compressor stall. The outputs of neural networks support the dynamic knowledge data base of an expert system. This is the open-loop mode to avoid compressor stall. The integration of a control system with neural networks is the closed-loop mode in stall avoidance. The feasibility of the concept has been demonstrated for the compressor of 16-foot transonic/supersonic propulsion wind tunnels. The construction of a prototpye expert system has been initiated.
This report deals with the object-oriented model development of a neuro-controller design for permanent magnet (PM) dc motor drives. The system under study is described as a collection of interacting objects. Each object module describes the object behaviors, called methods. The characteristics of the object are included in its variables. The knowledge of the object exists within its variables, and the performance is determined by its methods. This structure maps well to the real world objects that comprise the system being modeled. A dynamic learning architecture that possesses the capabilities of simultaneous on-line identification and control is incorporated to enforce constraints on connections and control the dynamics of the motor. The control action is implemented "on-line", in "real time" in such a way that the predicted trajectory follows a specified reference model. A design example of controlling a PM dc motor drive on-line shows the effectiveness of the design tool. This will therefore be very useful in aerospace applications. It is expected to provide an innovative and noval software model for the rocket engine numerical simulator executive.
The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.
The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: 1) Overview of Adaptive Systems; and 2) V&V Processes/Methods.
The purpose of this note is to show how hybrid systems may be modeled as products of nonlinear control systems and finite state automata. By a hybrid system, we mean a network of consisting of continuous, nonlinear control system connected to discrete, finite state automata. Our point of view is that the automata switches between the control systems, and that this switching is a function of the discrete input symbols or letters that it receives. We show how a nonlinear control system may be viewed as a pair consisting of a bialgebra of operators coding the dynamics, and an algebra of observations coding the state space. We also show that a finite automata has a similar representation. A hybrid system is then modeled by taking suitable products of the bialgebras coding the dynamics and the observation algebras coding the state spaces.
The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.
Mission control center with network of ground stations to provide display, control and communication services
This paper presents basic network control concepts for the Advanced Communications Technology Satellite (ACTS) System. Two experimental systems, called the low-burst-rate and high-burst-rate systems, along with ACTS ground system features, are described. The network control issues addressed include frame structures, acquisition and synchronization procedures, coordinated station burst-time plan and satellite-time plan changes, on-board clock control based on ground drift measurements, rain fade control by means of adaptive forward-error-correction (FEC) coding and transmit power augmentation, and reassignment of channel capacities on demand. The NASA ground system, which includes a primary station, diversity station, and master control station, is also described.
NASA's Ikhana unmanned aerial vehicle (UAV) is a General Atomics MQ-9 Predator-B modified to support the conduct of Earth science missions for the NASA Science Mission Directorate through partnerships, other government agencies and universities. Ikhana, a Native American word meaning 'intelligence', can carry over 2000 lbs of atmospheric and remote sensing instruments in the payload bay and external pods. The aircraft is capable of mission durations in excess of 24 hours at altitudes above 40,000 ft. Redundant flight control, avionics, power, and network systems increase the system reliability and allow easier access to public airspace. The aircraft is remotely piloted from a mobile ground control station (GCS) using both C-band line-of-sight and Ku-band over-the-horizon satellite datalinks. NASA's GCS has been modified to support on-site science monitoring, or the downlink data can be networked to remote sites. All ground support systems are designed to be deployable to support global Eart science investigations. On-board support capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The ARTS can host research algorithms that will autonomously command and control on-board sensors, perform sensor health monitoring, conduct data analysis, and request changes to the flight plan to maximize data collection. The ARTS also has the ability to host algorithms that will autonomously control the aircraft trajectory based on sensor needs, (e.g. precision trajectory for repeat pass interferometry) or to optimize mission objectives (e.g. search for specific atmospheric conditions). Standard on-board networks will collect science data for recording and for inclusion in the aircraft's high bandwidth downlink. The Ikhana project will complete GCS development, science support systems integration, external pod integration and flight clearance, and operations crew training in early 2007. A large-area remote sensing mission is currently scheduled for the Summer 2007.
Humanoid robots have similar physical builds and motion patterns as humans. Not only does this provide a suitable operating environment for the humanoid but it also opens up many research doors on how humans function. The overall objective is replacing humans operating in unsafe environments. A first target application is assembly of structures for future lunar-planetary bases. The initial development platform is a Fujitsu HOAP-2 humanoid robot. The goal for the project is to demonstrate the capability of a HOAP-2 to autonomously construct a cubic frame using provided tubes and joints. This task will require the robot to identify several items, pick them up, transport them to the build location, then properly assemble the structure. The ability to grasp and assemble the pieces will require improved motor control and the addition of tactile feedback sensors. In recent years, learning-based control is becoming more and more popular; for implementing this method we will be using the Adaptive Neural Fuzzy Inference System (ANFIS). When using neural networks for control, no complex models of the system must be constructed in advance-only input/output relationships are required to model the system.