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State equations arising in the description of power processing systems are described. The role played by Lie groups and Lie algebras in characterizing the inherent dynamical features of these systems is outlined, and network examples are presented for illustration.
Fluid systems, or networks, consist of multiple components that work together to achieve some desired thermofluid state. For any generic application, this fluid state can be a combination of the fluid pressure, flow rate, enthalpy, or species concentration. Fluid system components, such as pumps and valves, are often governed by nonlinear differential equations, resulting in complex component-to-component interactions. System-level fluid network stability occurs when the flow through the system can maintain a steady-state solution in the presence of small perturbations, which depends on these component interactions. System instability, however, can go undetected until issues arise during integrated system testing. This presentation explores a method for system designers to think of the fluid network as an assembly of components, each with their own thermofluid surfaces of partial stability, called nullclines. The intersections of all nullclines yields system-level solutions, called equilibrium points. When designers define operating points, they are tuning system parameters so that these equilibrium points move to the desired location in the thermofluid state plane. However, linearization theory shows us that the dynamic behavior around these points can be unstable. The local stability of these equilibrium points can be assessed analytically with eigen-analysis, or numerically by propagating state-plane samples to construct a phase portrait. Investigating a phase portrait can help designers gain a qualitative understanding of a system’s dynamic performance. This understanding can then help inform requirement definitions, component selection, and operational procedures. This presentation includes an example of the phase portrait technique on a system featuring a centrifugal pump and a back-pressure regulator (BPR). Numerical modeling of this system suggests that equilibrium points on the left-hand side of the pump curve are dynamically unstable.
In this paper, the XNsim approach to achieve Internet-enabled, dynamically scalable collaborative distributed simulation capabilities is presented. With this approach, a complete simulation can be assembled from shared component subsystems written in different formats, that run on different computing platforms, with different sampling rates, in different geographic locations, and over singlelmultiple networks. The subsystems interact securely with each other via the Internet. Furthermore, the simulation topology can be dynamically modified. The distributed simulation uses a combination of hub-and-spoke and peer-topeer network topology. A proof-of-concept demonstrator is also presented. The XNsim demonstrator can be accessed at http://www.jsc.draver.corn/xn that hosts various examples of Internet enabled simulations.
Network synthesis technique applied to structural dynamics design, using topological formulas to express systems response transfer functions
On-line programming for the following topics is discussed: (1) artificial intelligence, (2) automata theory, (3) cellular automata, (4) computation structures, (5) computer systems research, (6) dynamic modeling, computer graphics, and computer networks, (7) education, (8) implicit computation, (10) interactive management systems, (11) interactive algebraic manipulation, and (12) programming languages.
A method is derived for digital computer simulation of linear time-invariant systems when the insignificant eigenvalues involved in such systems are eliminated by an ALSAP root removal technique. The method is applied to a thirteenth-order dynamic system representing a passive RLC network.
Eight dual channel microwave radiometers were constructed as a research and development effort for the Crustal Dynamics Project and the Deep Space Network. These instruments, known as water vapor radiometers, are primarily intended to demonstrate that the variable path delay imposed by atmospheric water vapor can be calibrated in microwave tracking and distance measuring systems but could also be used in other applications involving moist air meteorology and propagation studies. They are being deployed to various stations and observatories that participate in Very Long Baseline Interferometry experiments. The development history of these instruments are reviewed, the theory of operation and overall design considerations are outlined, and the instrumental parameters and performance characteristics are described.
Computer programs for degaussing, magnetic field calculation, low speed wing flap systems aerodynamics, structural panel analysis, dynamic stress/strain data acquisition, allocation and network scheduling, and digital filters are discussed.
Transmission electron microscopy and optical microscopy were used to examine polycrystalline specimens of the B2-structured alloy FeAl strained under tension to fracture at elevated temperature. Strain-induced grain boundary migration was observed above 900 K and dynamic recrystallization was found at 1000 K and 1100 K. Little evidence of dynamic recovery was evident but some networks were formed at 1100 K.
A technique for the efficient analytical computation of such parameters of the neural architecture as synaptic weights and neural gain is presented as a single solution of a set of adjoint equations. The learning model discussed concentrates on the adiabatic approximation only. A problem of interest is represented by a system of N coupled equations, and then adjoint operators are introduced. A neural network is formalized as an adaptive dynamical system whose temporal evolution is governed by a set of coupled nonlinear differential equations. An approach based on the minimization of a constrained neuromorphic energylike function is applied, and the complete learning dynamics are obtained as a result of the calculations.
In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.
Emerging and next-generation test instrumentation increasingly relies on network communication to manage complex and dynamic test scenarios, particularly for uninhabited autonomous systems. Adapting wireless communication infrastructure to accommodate challenging testing needs can benefit from reconfigurable radio technology. Frequency agility is one characteristic of reconfigurable radios that to date has seen only limited progress toward programmability. This paper overviews an ongoing project to validate a promising chipset that performs conversion of RF signals directly into digital data for the wireless receiver and, for the transmitter, converts digital data into RF signals. The Software Configurable Multichannel Transceiver (SCMT) enables four transmitters and four receivers in a single unit, programmable for any frequency band between 1 MHz and 6 GHz.
This non-destructive, optical fatigue detection and monitoring system relies on a small and unobtrusive light-scattering sensor that is installed on a component at the beginning of its life in order to periodically scan the component in situ. The method involves using a laser beam to scan the surface of the monitored component. The device scans a laser spot over a metal surface to which it is attached. As the laser beam scans the surface, disruptions in the surface cause increases in scattered light intensity. As the disruptions in the surface grow, they will cause the light to scatter more. Over time, the scattering intensities over the scanned line can be compared to detect changes in the metal surface to find cracks, crack precursors, or corrosion. This periodic monitoring of the surface can be used to indicate the degree of fatigue damage on a component and allow one to predict the remaining life and/or incipient mechanical failure of the monitored component. This wireless, compact device can operate for long periods under its own battery power and could one day use harvested power. The prototype device uses the popular open-source TinyOS operating system on an off-the-shelf Mica2 sensor mote, which allows wireless command and control through dynamically reconfigurable multi-node sensor networks. The small size and long life of this device could make it possible for the nodes to be installed and left in place over the course of years, and with wireless communication, data can be extracted from the nodes by operators without physical access to the devices. While a prototype has been demonstrated at the time of this reporting, further work is required in the system s development to take this technology into the field, especially to improve its power management and ruggedness. It should be possible to reduce the size and sensitivity as well. Establishment of better prognostic methods based on these data is also needed. The increase of surface roughness with fatigue is closely connected to the microstructure of the metal, and ongoing research is seeking to connect this observed evidence of the fatigue state with microstructural theories of fatigue evolution to allow more accurate prognosis of remaining component life. Plans are also being discussed for flight testing, perhaps on NASA s SOFIA platform.
In this work, we develop an approach to formation estimation by explicitly characterizing formation's system-theoretic attributes in terms of the underlying inter-spacecraft information-exchange network. In particular, we approach the formation observer/estimator design by relaxing the accessibility to the global state information by a centralized observer/estimator- and in turn- providing an analysis and synthesis framework for formation observers/estimators that rely on local measurements. The noveltyof our approach hinges upon the explicit examination of the underlying distributed spacecraft network in the realm of guidance, navigation, and control algorithmic analysis and design. The overarching goal of our general research program, some of whose results are reported in this paper, is the development of distributed spacecraft estimation algorithms that are scalable, modular, and robust to variations inthe topology and link characteristics of the formation information exchange network. In this work, we consider the observability of a spacecraft formation from a single observation node and utilize the agreement protocol as a mechanism for observing formation states from local measurements. Specifically, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the observability of the corresponding multi-agent system The ramification of this notion of observability over networks is then explored in the context of distributed formation estimation.
In this paper a fuzzy classification procedure is applied to polarimetric radar measurements, and street pixels are detected.
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For planetary lander missions, the most challenging phase of the spacecraft to ground communications is during the entry, descent, and landing (EDL). As each 2003 Mars Exploration Rover (MER) enters the Martian atmosphere, it slows dramatically. The extreme acceleration and jerk cause extreme Doppler dynamics on the X-band signal received on Earth. When the vehicle slows sufficiently, the parachute is deployed, causing almost a step in deceleration. After parachute deployment, the lander is lowered beneath the parachute on a bridle. The swinging motion of the lander imparts high Doppler dynamics on the signal and causes the received signal strength to vary widely, due to changing antenna pointing angles. All this time, the vehicle transmits important health and status information that is especially critical if the landing is not successful. Even using the largest Deep Space Network antennas, the weak signal and high dynamics render it impossible to conduct reliable phase coherent communications. Therefore, a specialized form of frequency-shift-keying will be used. This paper describes the EDL scenario, the signal conditions, the methods used to detect and frequency-track the carrier and to detect the data modulation, and the resulting performance estimates.
The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.