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At least 1,315 records · Page 73

Hybrid computer methods for direct functional optimization

Control and trajectory optimization involves the minimization of a performance index (PI) of integral form where some optimal control law exists in a dynamic system. In this paper, a hybrid minicomputer with an adaptive random-search algorithm implements an iterative search for the optimal control. The search assumes that some initial control is randomly perturbed and a fast analog computer generates respective PI from the analog response of the dynamic system. An improved PI informs the digital computer to utilize the perturbed control as a basis for the next iteration; otherwise a new perturbation replaces the old perturbation in the next iteration. The search terminates when no further improvements occur.

Andrews, M.↗

Application of Consider Covariance to the Extended Kalman Filter

The extended Kalman filter (EKF) is the basis for many applications of filtering theory to real-time problems where estimates of the state of a dynamical system are to be computed based upon some set of observations. The form of the EKF may vary somewhat from one application to another, but the fundamental principles are typically unchanged among these various applications. As is the case in many filtering applications, models of the dynamical system (differential equations describing the state variables) and models of the relationship between the observations and the state variables are created. These models typically employ a set of constants whose values are established my means of theory or experimental procedure. Since the estimates of the state are formed assuming that the models are perfect, any modeling errors will affect the accuracy of the computed estimates. Note that the modeling errors may be errors of commission (errors in terms included in the model) or omission (errors in terms excluded from the model). Consequently, it becomes imperative when evaluating the performance of real-time filters to evaluate the effect of modeling errors on the estimates of the state.

Lundberg, John B.↗

Zero-Gravity Locomotion Simulators: New Ground-Based Analogs for Microgravity Exercise Simulation

Maintaining health and fitness in crewmembers during space missions is essential for preserving performance for mission-critical tasks. NASA's Exercise Countermeasures Project (ECP) provides space exploration exercise hardware and monitoring requirements that lead to devices that are reliable, meet medical, vehicle, and habitat constraints, and use minimal vehicle and crew resources. ECP will also develop and validate efficient exercise prescriptions that minimize daily time needed for completion of exercise yet maximize performance for mission activities. In meeting these mission goals, NASA Glenn Research Center (Cleveland, OH, USA), in collaboration with the Cleveland Clinic (Cleveland, Ohio, USA), has developed a suite of zero-gravity locomotion simulators and associated technologies to address the need for ground-based test analog capability for simulating in-flight (microgravity) and surface (partial-gravity) exercise to advance the health and safety of astronaut crews and the next generation of space explorers. Various research areas can be explored. These include improving crew comfort during exercise, and understanding joint kinematics and muscle activation pattern differences relative to external loading mechanisms. In addition, exercise protocol and hardware optimization can be investigated, along with characterizing system dynamic response and the physiological demand associated with advanced exercise device concepts and performance of critical mission tasks for Exploration class missions. Three zero-gravity locomotion simulators are currently in use and the research focus for each will be presented. All of the devices are based on a supine subject suspension system, which simulates a reduced gravity environment by completely or partially offloading the weight of the exercising test subject s body. A platform for mounting treadmill is positioned perpendicularly to the test subject. The Cleveland Clinic Zero-g Locomotion Simulator (ZLS) utilizes a pneumatic subject load device to apply a near constant gravity-replacement load to the test subject during exercise, and is currently used in conjunction with the General Clinical Research Center for evaluating exercise protocols using a bedrest analog. The enhanced ZLS (eZLS) at NASA Glenn Research Center features an offloaded treadmill that floats on a thin film of air and interfaces to a force reaction frame via variably-compliant isolators, or vibration isolation system. The isolators can be configured to simulate compliant interfaces to the vehicle, which affects mechanical loading to crewmembers during exercise, and has been used to validate system dynamic models for new countermeasures equipment designs, such as the second International Space Station treadmill slated for use in 2010. In the eZLS, the test subject and exercise device can be pitched at the appropriate angle for partial gravity simulations, such as lunar gravity (1/6th earth gravity). On both the eZLS and the NASA-Johnson Space Center standalone ZLS installed at the University of Texas Medical Branch in Galveston, Texas, USA, the subject's body weight relative to the treadmill is controlled via a linear motor subject load device (LM-SLD). The LM-SLD employs a force-feedback closed-loop control system to provide a relatively constant force to the test subject during locomotion, and is set and verified for subject safety prior to each session. Locomotion data were collected during parabolic flight and on the eZLS. The purpose was to determine the similarities and differences between locomotion in actual and simulated microgravity. Subjects attained greater amounts of hip flexion during walking and running during parabolic flight. During running, subjects had greater hip range of motion. Trunk motion was significantly less on the eZLS than during parabolic flight. Peak impact forces, loading rate, and impulse were greater on the eZLS than during parabolic while walking with a low external load (EL) and rning with a high EL. Activation timing differences existed between locations in all muscles except for the rectus femoris. The tibialis anterior and gluteus maximus were active for longer durations on the eZLS than in parabolic flight during walking. Ground reaction forces were greater with the LM-SLD than with bungees during eZLS locomotion. While the eZLS serves as a ground-based analog, researchers should be aware that subtle, but measurable, differences in kinematics and leg musculature activities exist between the environments. Aside from space applications, zero-gravity locomotion simulators may help medical researchers in the future with development of rehabilitative or therapeutic protocols for injured or ill patients. Zero-gravity locomotion simulators may be used as a ground-based test bed to support future missions for space exploration, and eventually may be used to simulate planetary locomotion in partial gravity environments, including the Moon and Mars. Figure: Zero-gravity Locomotion Simulator at the Cleveland Clinic, Cleveland, Ohio, USA

Perusek, Gail P.↗

Decentralized Carrier Interleaving in Cascaded Multilevel DC-AC Converters: Preprint

Cascaded dc-ac converters with interleaved carriers are commonly used in applications where elevated voltages and low-distortion multilevel waveforms are needed. Example applications include modular multilevel converters and solid-state transformers. In such systems, carrier interleaving is generally achieved via communication among the stacked converters or a centralized controller. Due to the large number of converters in such a system, existing approaches entail significant wiring complexity and communication may limit resilience to failures. In this paper, we introduce a control strategy which achieves communication-free carrier interleaving among series-connected converters. The proposed controller is embedded within each converter control loop and only requires measurements available at each set of converter terminals. We formulate a dynamic system model and show that the system converges to the interleaved condition irrespective of the number of converters in the stack. After outlining a practical method for digital implementation, experiments are shown on a hardware-in-the-loop setup.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Lyapunov exponents from CHUA's circuit time series using artificial neural networks

In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

Gonzalez, J. Jesus↗

High-speed data acquisition for the Princeton University Dynamic Model Track

Real time analysis of data can reduce the time involved in exploring dynamic systems. The failure of the data acquisition system at the Princeton Dynamic Model Track prompted its replacement with a real time data acquisition system. Data can be obtained from an experiment and analyzed during and immediately following a data run. The new system employs high speed analog to digital conversion and a small computer to collect data. Sampling rates of 1000 hertz over 44 channels (44,000 words/sec) are obtainable. The data can be accessed as it enters the computer's environment where it may be displayed or stored for later processing. The system was tested on a helicopter rotor steep descent experiment. The data collected compares with previous data from a similar experiment.

Demko, Michael G.↗

Performance and stability of telemanipulators using bilateral impedance control

A new method of control for telemanipulators called bilateral impedance control is investigated. This new method differs from previous approaches in that interaction forces are used as the communication signals between the master and slave robots. The new control architecture has several advantages: (1) It allows the master robot and the slave robot to be stabilized independently without becoming involved in the overall system dynamics; (2) It permits the system designers to arbitrarily specify desired performance characteristics such as the force and position ratios between the master and slave; (3) The impedance at both ends of the telerobotic system can be modulated to suit the requirements of the task. The main goals of the research are to characterize the performance and stability of the new control architecture. The dynamics of the telerobotic system are described by a bond graph model that illustrates how energy is transformed, stored, and dissipated. Performance can be completely described by a set of three independent parameters. These parameters are fundamentally related to the structure of the H matrix that regulates the communication of force signals within the system. Stability is analyzed with two mathematical techniques: the Small Gain Theorem and the Multivariable Nyquist Criterion. The theoretical predictions for performance and stability are experimentally verified by implementing the new control architecture on a multidegree of freedom telemanipulator.

Moore, Christopher Lane↗

Architectural Analysis of Dynamically Reconfigurable Systems

oTpics include: the problem (increased flexibility of architectural styles decrease analyzability, behavior emerges and varies depending on the configuration, does the resulting system run according to the intended design, and architectural decisions can impede or facilitate testing); top down approach to architecture analysis, detection of defects and deviations, and architecture and its testability; currently targeted projects GMSEC and CFS; analyzing software architectures; analyzing runtime events; actual architecture recognition; GMPUB in Dynamic SAVE; sample output from new approach; taking message timing delays into account; CFS examples of architecture and testability; some recommendations for improved testablity; and CFS examples of abstract interfaces and testability; CFS example of opening some internal details.

Lindvall, Mikael↗

Energy prediction using spatiotemporal pattern networks

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Deterministic representation of chaos with application to turbulence

Chaotic motions of nonlinear dynamical systems are decomposed into mean components and fluctuations. The approach is based upon the concept that the fluctuations driven by the instability of the original (unperturbed) motion grow until a new stable state is approached. The Reynolds-type equations written for continuous as well as for finite-degrees-of-freedom dynamical systems are closed by using this stabilization principle. The theory is applied to conservative systems, to strange attractors and to turbulent motions.

Zak, M.↗

Functional expansion representations of artificial neural networks

In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

Gray, W. Steven↗

Theory and Simulation of Real and Ideal Magnetohydrodynamic Turbulence

Incompressible, homogeneous magnetohydrodynamic (MHD) turbulence consists of fluctuating vorticity and magnetic fields, which are represented in terms of their Fourier coefficients. Here, a set of five Fourier spectral transform method numerical simulations of two-dimensional (2-D) MHD turbulence on a 512(sup 2) grid is described. Each simulation is a numerically realized dynamical system consisting of Fourier modes associated with wave vectors k, with integer components, such that k = |k| less than or equal to k(sub max). The simulation set consists of one ideal (non-dissipative) case and four real (dissipative) cases. All five runs had equivalent initial conditions. The dimensions of the dynamical systems associated with these cases are the numbers of independent real and imaginary parts of the Fourier modes. The ideal simulation has a dimension of 366104, while each real simulation has a dimension of 411712. The real runs vary in magnetic Prandtl number P(sub M), with P(sub M) is a member of {0.1, 0.25, 1, 4}. In the results presented here, all runs have been taken to a simulation time of t = 25. Although ideal and real Fourier spectra are quite different at high k, they are similar at low values of k. Their low k behavior indicates the existence of broken symmetry and coherent structure in real MHD turbulence, similar to what exists in ideal MHD turbulence. The value of PM strongly affects the ratio of kinetic to magnetic energy and energy dissipation (which is mostly ohmic). The relevance of these results to 3-D Navier-Stokes and MHD turbulence is discussed.

Shebalin, John V.↗

Data assimilation in operator algebras

We develop an algebraic framework for sequential data assimilation of partially observed dynamical systems. In this framework, Bayesian data assimilation is embedded in a nonabelian operator algebra, which provides a representation of observables by multiplication operators and probability densities by density operators (quantum states). In the algebraic approach, the forecast step of data assimilation is represented by a quantum operation induced by the Koopman operator of the dynamical system. Moreover, the analysis step is described by a quantum effect, which generalizes the Bayesian observational update rule. Projecting this formulation to finite-dimensional matrix algebras leads to computational schemes that are i) automatically positivity-preserving and ii) amenable to consistent data-driven approximation using kernel methods for machine learning. Moreover, these methods are natural candidates for implementation on quantum computers. Applications to the Lorenz 96 multiscale system and the El Niño Southern Oscillation in a climate model show promising results in terms of forecast skill and uncertainty quantification.

97 MATHEMATICS AND COMPUTING↗

Multiscale modeling high-order methods and data-driven modeling

Projection-based reduced-order models (ROMs) comprise a promising set of data-driven approaches for accelerating the simulation of high-fidelity numerical simulations. Standard projection-based ROM approaches, however, suffer from several drawbacks when applied to the complex nonlinear dynamical systems commonly encountered in science and engineering. These limitations include a lack of stability, accuracy, and sharp a posteriori error estimators. This work addresses these limitations by leveraging multiscale modeling, least-squares principles, and machine learning to develop novel reduced-order modeling approaches, along with data-driven a posteriori error estimators, for dynamical systems. Theoretical and numerical results demonstrate that the two ROM approaches developed in this work - namely the windowed least-squares method and the Adjoint Petrov - Galerkin method - yield substantial improvements over state-of-the-art approaches. Additionally, numerical results demonstrate the capability of the a posteriori error models developed in this work.

97 MATHEMATICS AND COMPUTING↗

Effects of Deployment Investment on the Growth of the Biofuels Industry

In support of the national goals for biofuel use in the United States, numerous technologies have been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion technology pathways are operating at commercial scales, while others are in earlier stages of development. The advancement of a new pathway toward commercialization involves various types of progress, including yield improvements, process engineering, and financial performance. Actions of private investors and public programs can accelerate the demonstration and deployment of new conversion technology pathways. These investors (both private and public) will pursue a range of pilot, demonstration, and pioneer scale biorefinery investments; the most cost-effective set of investments for advancing the maturity of any given biomass to biofuel conversion technology pathway is unknown. In some cases, whether or not the pathway itself will ultimately be technically and financially successful is also unknown. This report presents results from the Biomass Scenario Model -- a system dynamics model of the biomass to biofuels system -- that estimate effects of investments in biorefineries at different maturity levels and operational scales. The report discusses challenges in estimating effects of such investments and explores the interaction between this deployment investment and a volumetric production incentive. Model results show that investments in demonstration and deployment have a substantial positive effect on the development of the biofuels industry. Results also show that other conditions, such as supportive policies, have major impacts on the effectiveness of such investments.

09 BIOMASS FUELS↗

Effects of Deployment Investment on the Growth of the Biofuels Industry. 2016 Update

This report updates the 2013 report of the same title. Some text originally published in that report is retained and indicated in gray. In support of the national goals for biofuel use in the United States, numerous technologies have been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion technology pathways are operating at commercial scales, while others are in earlier stages of development. The advancement of a new pathway toward commercialization involves various types of progress, including yield improvements, process engineering, and financial performance. Actions of private investors and public programs can accelerate the demonstration and deployment of new conversion technology pathways. These investors (both private and public) will pursue a range of pilot, demonstration, and pioneer scale biorefinery investments; the most cost-effective set of investments for advancing the maturity of any given biomass to biofuel conversion technology pathway is unknown. In some cases, whether or not the pathway itself will ultimately be technically and financially successful is also unknown. This report presents results from the Biomass Scenario Model--a system dynamics model of the biomass to biofuels system--that estimate effects of investments in biorefineries at different maturity levels and operational scales. The report discusses challenges in estimating effects of such investments and explores the interaction between this deployment investment and a volumetric production incentive. Model results show that investments in demonstration and deployment have a substantial growth impact on the development of the biofuels industry. Results also show that other conditions, such as accompanying incentives, have major impacts on the effectiveness of such investments. Results from the 2013 report are compared to new results. This report does not advocate for or against investments, incentives, or policies, but analyzes simulations of their hypothetical effects.

09 BIOMASS FUELS↗

Expectation-Based Control of Noise and Chaos

A proposed approach to control of noise and chaos in dynamic systems would supplement conventional methods. The approach is based on fictitious forces composed of expectations governed by Fokker-Planck or Liouville equations that describe the evolution of the probability densities of the controlled parameters. These forces would be utilized as feedback control forces that would suppress the undesired diffusion of the controlled parameters. Examples of dynamic systems in which the approach is expected to prove beneficial include spacecraft, electronic systems, and coupled lasers.

Zak, Michael↗