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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob↗
NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob J.↗
NASA NTRS2003

Modeling beta-adrenergic control of cardiac myocyte contractility in silico

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Non-NASA Center↗
NASA NTRS1998

Neural Networks for Rapid Design and Analysis

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Sparks, Dean W., Jr.↗
NASA NTRS2004

Generation of Higher Order Modes in a Rectangular Duct

Advanced noise control methodologies to reduce sound emission from aircraft engines take advantage of the modal structure of the noise in the duct. This noise is caused by the interaction of rotor wakes with downstream obstructions such as exit guide vanes. Mode synthesis has been accomplished in circular ducts and current active noise control work has made use of this capability to cancel fan noise. The goal of the current effort is to examine the fundamental process of higher order mode propagation through an acoustically treated, curved duct. The duct cross-section is rectangular to permit greater flexibility in representation of a range of duct curvatures. The work presented is the development of a feedforward control system to generate a user-specified modal pattern in the duct. The multiple-error, filtered-x LMS algorithm is used to determine the magnitude and phase of signal input to the loudspeakers to produce a desired modal pattern at a set of error microphones. Implementation issues, including loudspeaker placement and error microphone placement, are discussed. Preliminary results from a 9-3/8 inch by 21 inch duct, using 12 loudspeakers and 24 microphones, are presented. These results demonstrate the ability of the control system to generate a user-specified mode while suppressing undesired modes.

Gerhold, Carl H.↗
NASA NTRS1997

Callosally projecting neurons in the macaque monkey V1/V2 border are enriched in nonphosphorylated neurofilament protein

Previous immunohistochemical studies combined with retrograde tracing in macaque monkeys have demonstrated that corticocortical projections can be differentiated by their content of neurofilament protein. The present study analyzed the distribution of nonphosphorylated neurofilament protein in callosally projecting neurons located at the V1/V2 border. All of the retrogradely labeled neurons were located in layer III at the V1/V2 border and at an immediately adjacent zone of area V2. A quantitative analysis showed that the vast majority (almost 95%) of these interhemispheric projection neurons contain neurofilament protein immunoreactivity. This observation differs from data obtained in other sets of callosal connections, including homotypical interhemispheric projections in the prefrontal, temporal, and parietal association cortices, that were found to contain uniformly low proportions of neurofilament protein-immunoreactive neurons. Comparably, highly variable proportions of neurofilament protein-containing neurons have been reported in intrahemispheric corticocortical pathways, including feedforward and feedback visual connections. These results indicate that neurofilament protein is a prominent neurochemical feature that identifies a particular population of interhemispheric projection neurons at the V1/V2 border and suggest that this biochemical attribute may be critical for the function of this subset of callosal neurons.

Non-NASA Center↗
NASA NTRS1997

Morphological evidence for local microcircuits in rat vestibular maculae

Previous studies suggested that intramacular, unmyelinated segments of vestibular afferent nerve fibers and their large afferent endings (calyces) on type I hair cells branch. Many of the branches (processes) contain vesicles and are presynaptic to type II hair cells, other processes, intramacular nerve fibers, and calyces. This study used serial section transmission electron microscopy and three-dimensional reconstruction methods to document the origins and distributions of presynaptic processes of afferents in the medial part of the adult rat utricular macula. The ultrastructural research focused on presynaptic processes whose origin and termination could be observed in a single micrograph. Results showed that calyces had 1) vesiculated, spine-like processes that invaginated type I cells and 2) other, elongate processes that ended on type II cells pre- as well as postsynaptically. Intramacular, unmyelinated segments of afferent nerve fibers gave origin to branches that were presynaptic to type II cells, calyces, calyceal processes, and other nerve fibers in the macula. Synapses with type II cells occurred opposite subsynaptic cisternae (C synapses); all other synapses were asymmetric. Vesicles were pleomorphic but were differentially distributed according to process origin. Small, clear-centered vesicles, approximately 40-60 nm in diameter, predominated in processes originating from afferent nerve fibers and basal parts of calyces. Larger vesicles approximately 70-120 nm in diameter having approximately 40-80 nm electron-opaque cores were dominant in processes originating from the necks of calyces. Results are interpreted to indicate the existence of a complex system of intrinsic feedforward (postsynaptic)-feedback (presynaptic) connections in a network of direct and local microcircuits. The morphological findings support the concept that maculae dynamically preprocess linear acceleratory information before its transmission to the central nervous system.

NASA Center ARC↗
NASA NTRS1997

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

NASA Discipline Regulatory Physiology↗
NASA NTRS2004

Intelligent, Robust Control of Deteriorated Turbofan Engines via Linear Parameter Varying Quadratic Lyapunov Function Design

A method for accommodating engine deterioration via a scheduled Linear Parameter Varying Quadratic Lyapunov Function (LPVQLF)-Based controller is presented. The LPVQLF design methodology provides a means for developing unconditionally stable, robust control of Linear Parameter Varying (LPV) systems. The controller is scheduled on the Engine Deterioration Index, a function of estimated parameters that relate to engine health, and is computed using a multilayer feedforward neural network. Acceptable thrust response and tight control of exhaust gas temperature (EGT) is accomplished by adjusting the performance weights on these parameters for different levels of engine degradation. Nonlinear simulations demonstrate that the controller achieves specified performance objectives while being robust to engine deterioration as well as engine-to-engine variations.

Turso, James A.↗
NASA NTRS1992

An optimal controller for an electric ventricular-assist device: theory, implementation, and testing

This paper addresses the development and testing of an optimal position feedback controller for the Penn State electric ventricular-assist device (EVAD). The control law is designed to minimize the expected value of the EVAD's power consumption for a targeted patient population. The closed-loop control law is implemented on an Intel 8096 microprocessor and in vitro test runs show that this controller improves the EVAD's efficiency by 15-21%, when compared with the performance of the currently used feedforward control scheme.

Therapy, Computer-Assisted/standards↗
NASA NTRS1998

Model-Trained Neural Networks and Electronic Holography Demonstrated to Detect Damage in Blades

Detect Damage in Blades Electronic holography can show damaged regions in fan blades at 30 frames/sec. The electronic holograms are transformed by finite-element-model-trained artificial neural networks to visualize the damage. The trained neural networks are linked with video and graphics to visualize the bending-induced strain distribution, which is very sensitive to damage. By contrast, it is very difficult to detect damage by viewing the raw, speckled, characteristic fringe patterns. For neural-network visualization of damage, 2 frames or 2 fields are used, rather than the 12 frames normally used to compute the displacement distribution from electronic holograms. At the NASA Lewis Research Center, finite element models are used to compute displacement and strain distributions for the vibration modes of undamaged and cracked blades. A model of electronic time-averaged holography is used to transform the displacement distributions into finite-element-resolution characteristic fringe patterns. Then, a feedforward neural network is trained with the fringe-pattern/strain-pattern pairs, and the neural network, electronic holography, and video are implemented on a workstation. Now that the neural networks have been tested successfully at 30 frames/sec on undamaged and cracked cantilevers, the electronic holography and neural-network processing are being adapted for onsite damage inspection of twisted fan blades and rotormounted blades. Our conclusion is that model-trained neural nets are effective when they are trained with good models whose application is well understood. This work supports the aeromechanical testing portion of the Advanced Subsonic Technology Project.

Decker, Arthur J.↗
NASA NTRS2006

Exploratory Studies in Generalized Predictive Control for Active Gust Load Alleviation

The results of numerical simulations aimed at assessing the efficacy of Generalized Predictive Control (GPC) for active gust load alleviation using trailing- and leading-edge control surfaces are presented. The equations underlying the method are presented and discussed, including system identification, calculation of control law matrices, and calculation of commands applied to the control effectors. Both embedded and explicit feedforward paths for inclusion of disturbance effects are addressed. Results from two types of simulations are shown. The first used a 3-DOF math model of a mass-spring-dashpot system subject to user-defined external disturbances. The second used open-loop data from a wind-tunnel test in which a wing model was excited by sinusoidal vertical gusts; closed-loop behavior was simulated in post-test calculations. Results obtained from these simulations have been decidedly positive. In particular, results of closed-loop simulations for the wing model showed reductions in root moments by factors as high as 1000, depending on whether the excitation is from a constant- or variable-frequency gust and on the direction of the response.

Kvaternik, Raymond G.↗
NASA NTRS2006

High Fidelity System Simulation of Multiple Components in Support of the UEET Program

The High Fidelity System Simulation effort has addressed various important objectives to enable additional capability within the NPSS framework. The scope emphasized High Pressure Turbine and High Pressure Compressor components. Initial effort was directed at developing and validating intermediate fidelity NPSS model using PD geometry and extended to high-fidelity NPSS model by overlaying detailed geometry to validate CFD against rig data. Both "feedforward" and feedback" approaches of analysis zooming was employed to enable system simulation capability in NPSS. These approaches have certain benefits and applicability in terms of specific applications "feedback" zooming allows the flow-up of information from high-fidelity analysis to be used to update the NPSS model results by forcing the NPSS solver to converge to high-fidelity analysis predictions. This apporach is effective in improving the accuracy of the NPSS model; however, it can only be used in circumstances where there is a clear physics-based strategy to flow up the high-fidelity analysis results to update the NPSS system model. "Feed-forward" zooming approach is more broadly useful in terms of enabling detailed analysis at early stages of design for a specified set of critical operating points and using these analysis results to drive design decisions early in the development process.

Plybon, Ronald C.↗
NASA NTRS2006

GOES-R STATIONKEEPING AND MOMENTUM MANAGEMENT

The NOAA Geostationary Operational Environmental Satellites (GOES) fire thrusters to remain within a 1deg longitude-latitude box and to dump accumulated angular momentum. In the past, maneuvers have disrupted GOES imaging due to attitude transients and the loss of orbit knowledge. If the R-series of spacecraft to be launched starting in 2012 were to follow current practice, maneuvers would still fail to meet Image Navigation and Registration (INR) specifications during and after thruster firings. Although maneuvers and recovery take only one percent of spacecraft lifetime, they sometimes come at inopportune times, such as hurricane season, when coverage is critical. To alleviate this problem, thruster firings small enough not to affect imaging are being considered. Eliminating post-maneuver recovery periods increases availability and facilitates autonomous operation. Frequent maneuvers also reduce 1ongitudeAatitude variation and allow satellite co-location. Improved orbit observations come from a high-altitude GPS receiver, and improved attitude control comes from thruster torque compensation. This paper reviews the effects of thruster firings on position knowledge and pointing control and suggests that low-thrust burns plus GPS and feedforward control offer a less disruptive approach to GOES-R stationkeeping and momentum management.

Chu, Donald↗
NASA NTRS2006

Development of an Experimental Rig for Investigation of Higher Order Modes in Ducts

Continued progress to reduce fan noise emission from high bypass ratio engine ducts in aircraft increasingly relies on accurate description of the sound propagation in the duct. A project has been undertaken at NASA Langley Research Center to investigate the propagation of higher order modes in ducts with flow. This is a two-pronged approach, including development of analytic models (the subject of a separate paper) and installation of a laboratory-quality test rig. The purposes of the rig are to validate the analytical models and to evaluate novel duct acoustic liner concepts, both passive and active. The dimensions of the experimental rig test section scale to between 25% and 50% of the aft bypass ducts of most modern engines. The duct is of rectangular cross section so as to provide flexibility to design and fabricate test duct liner samples. The test section can accommodate flow paths that are straight through or offset from inlet to discharge, the latter design allowing investigation of the effect of curvature on sound propagation and duct liner performance. The maximum air flow rate through the duct is Mach 0.3. Sound in the duct is generated by an array of 16 high-intensity acoustic drivers. The signals to the loudspeaker array are generated by a multi-input/multi-output feedforward control system that has been developed for this project. The sound is sampled by arrays of flush-mounted microphones and a modal decomposition is performed at the frequency of sound generation. The data acquisition system consists of two arrays of flush-mounted microphones, one upstream of the test section and one downstream. The data are used to determine parameters such as the overall insertion loss of the test section treatment as well as the effect of the treatment on a modal basis such as mode scattering. The methodology used for modal decomposition is described, as is a description of the mode generation control system. Data are presented which demonstrate the performance of the controller to generate the desired mode while suppressing all other cut on modes in the duct.

Gerhold, Carl H.↗
NASA NTRS2001

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

cerebellum cerebellar learning dynamic state estim↗
NASA NTRS2007

Entry Attitude Controller for the Mars Science Laboratory

This paper describes the preliminary concept for the RCS 3-axis attitude controller for the exo-atmospheric and guided entry phases of the Mars Science Laboratory Entry, Descend and Landing. The entry controller is formulated as three independent channels in the control frame, which is nominally aligned with the stability frame. Each channel has a feedfoward and a feedback. The feedforward path enables fast response to large bank commands. The feedback path stabilizes the vehicle angle of attack and sideslip around its trim position, and tracks bank commands. The feedback path has a PD/D structure with deadbands that minimizes fuel usage. The performance of this design is demonstrated via simulation.

Mars↗
NASA NTRS1991

Dual-arm manipulators with adaptive control

The described and improved multi-arm invention of this application presents three strategies for adaptive control of cooperative multi-arm robots which coordinate control over a common load. In the position-position control strategy, the adaptive controllers ensure that the end-effector positions of both arms track desired trajectories in Cartesian space despite unknown time-varying interaction forces exerted through a load. In the position-hybrid control strategy, the adaptive controller of one arm controls end-effector motions in the free directions and applied forces in the constraint directions; while the adaptive controller of the other arm ensures that the end-effector tracks desired position trajectories. In the hybrid-hybrid control strategy, the adaptive controllers ensure that both end-effectors track reference position trajectories while simultaneously applying desired forces on the load. In all three control strategies, the cross-coupling effects between the arms are treated as disturbances which are compensated for by the adaptive controllers while following desired commands in a common frame of reference. The adaptive controllers do not require the complex mathematical model of the arm dynamics or any knowledge of the arm dynamic parameters or the load parameters such as mass and stiffness. Circuits in the adaptive feedback and feedforward controllers are varied by novel adaptation laws.

Seraji, Homayoun↗