Search NASA⌕ Search

SEARCH · Search NASA

Results for “dynamic state estimation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Online State Estimation for Time-Varying Systems

The paper investigates the problem of estimating the state of a time-varying system with a linear measurement model; in particular, the paper considers the case where the number of measurements available can be smaller than the number of states. In lieu of a batch linear least-squares (LS) approach well-suited for static networks, where a sufficient number of measurements could be collected to obtain a full-rank design matrix the paper proposes an online algorithm to estimate the possibly time-varying state by processing measurements as and when available. The design of the algorithm hinges on a generalized LS cost augmented with a proximal-point-type regularization. With the solution of the regularized LS problem available in closed-form, the online algorithm is written as a linear dynamical system where the state is updated based on the previous estimate and based on the new available measurements. Conditions under which the algorithmic steps are in fact a contractive mapping are shown, and bounds on the estimation error are derived for different noise models. Numerical simulations are provided to corroborate the analytical findings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1987

Discrete filtering techniques applied to sequential GPS range measurements

The basic navigation solution is described for position and velocity based on range and delta range (Doppler) measurements from NAVSTAR Global Positioning System satellites. The application of discrete filtering techniques is examined to reduce the white noise distortions on the sequential range measurements. A second order (position and velocity states) Kalman filter is implemented to obtain smoothed estimates of range by filtering the dynamics of the signal from each satellite separately. Test results using a simulated GPS receiver show a steady-state noise reduction, the input noise variance divided by the output noise variance, of a factor of four. Recommendations for further noise reduction based on higher order Kalman filters or additional delta range measurements are included.

Vangraas, Frank↗
DOE OSTI2017

An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks

We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable of tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.

Jalving, Jordan↗
NASA NTRS1987

Investigation, development, and application of optimal output feedback theory. Volume 3: The relationship between dynamic compensators and observers and Kalman filters

Relationships between observers, Kalman Filters and dynamic compensators using feedforward control theory are investigated. In particular, the relationship, if any, between the dynamic compensator state and linear functions of a discrete plane state are investigated. It is shown that, in steady state, a dynamic compensator driven by the plant output can be expressed as the sum of two terms. The first term is a linear combination of the plant state. The second term depends on plant and measurement noise, and the plant control. Thus, the state of the dynamic compensator can be expressed as an estimator of the first term with additive error given by the second term. Conditions under which a dynamic compensator is a Kalman filter are presented, and reduced-order optimal estimaters are investigated.

Broussard, John R.↗
NASA NTRS1991

Precise time dissemination and applications development on the Bonneville Power Administration system

The Bonneville Power Administration (BPA) uses IRIG-B transmitted over microwave as its primary system time dissemination. Problems with accuracy and reliability have led to ongoing research into better methods. BPA has also developed and deployed a unique fault locator which uses precise clocks synchronized by a pulse over microwaves. It automatically transmits the data to a central computer for analysis. A proposed system could combine fault location timing and time dissemination into a Global Position System (GPS) timing receiver and close the verification loop through a master station at the Dittmer Control Center. Such a system would have many advantages, including lower cost, higher reliability, and wider industry support. Test results indicate the GPS has sufficient accuracy and reliability for this and other current timing requirements including synchronous phase angle measurements. A phasor measurement system which provides phase angle has recently been tested with excellent results. Phase angle is a key parameter in power system control applications including dynamic braking, DC modulation, remedial action schemes, and system state estimation. Further research is required to determine the applications which can most effectively use real-time phase angle measurements and the best method to apply them.

K. E. Martin↗
NASA NTRS1994

Steady and unsteady blade stresses within the SSME ATD/HPOTP inducer

There were two main goals of the ATD HPOTP (alternate turbopump development)(high pressure oxygen turbopump). First, determine the steady and unsteady inducer blade surface strains produced by hydrodynamic sources as a function of flow capacity (Q/N), suction specific speed (Nss), and Reynolds number (Re). Second, to identify the hydrodynamic source(s) of the unsteady blade strains. The reason the aforementioned goals are expressed in terms of blade strains as opposed to blade hydrodynamic pressures is because of the interest regarding the high cycle life of the inducer blades. This report focuses on the first goal of the test program which involves the determination of the steady and unsteady strain (stress) values at various points within the inducer blades. Strain gages were selected as the strain measuring devices. Concurrent with the experimental program, an analytical study was undertaken to produce a complete NASTRAN finite-element model of the inducer. Computational fluid dynamics analyses were utilized to provide the estimated steady-state blade surface pressure loading needed as load input to the NASTRAN inducer model.

Gross, R. Steven↗
DOE OSTI2015

Estimation of Future PM2.5- and Ozone-related Mortality over the Continental United States in a Changing Climate: An application of High-resolution Dynamical Downscaling Technique

This paper evaluates the PM2.5- and ozone-related mortality at present (2000s) and in the future (2050s) over the continental United States by using the Environmental Benefits Mapping and Analysis Program (BenMAP-CE). Atmospheric chemical fields are simulated by WRF/CMAQ (horizontal resolution: 12 × 12km), applying the dynamical downscaling technique from global climate-chemistry models under the Representative Concentration Pathways scenario (RCP 8.5). Future air quality results predict that the annual mean PM2.5 concentrations in continental US will decrease nationwide, especially in the eastern US and west coast. However, the ozone concentration is projected to decrease in the Eastern US but increase in the Western US. Future mortality is evaluated under two scenarios (1) holding future population and baseline incidence rate at the present level and (2) decreasing the future baseline incidence rate but increasing the future population. For PM2.5, the entire continental US presents a decreasing trend of PM2.5-related mortality by the 2050s in Scenario (1), primarily resulting from the emissions reduction. While in Scenario (2), almost half of the continental states show a rising tendency of PM2.5-related mortality, due to the dominant influence of population growth. In particular, the highest PM2.5-related deaths and the biggest discrepancy between present and future PM2.5-related deaths will both occur in California in 2050s. For the ozone-related premature mortality, the simulation shows nation-wide rising tendency in 2050s under both two scenarios, mainly due to the increase of ozone concentration and population in the future. Furthermore, the uncertainty analysis shows that the effect of the all causes mortality is much larger than for specific causes. This assessment is the result of the accumulated uncertainty of generating datasets. The uncertainty range of ozone-related all cause premature mortality is narrower than the PM2.5-related all cause mortality, due to its smaller standard deviation of beta parameter.

BenMAP-CE↗
DOE OSTI2025

Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning

Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing sea states. This study presents a novel control approach that leverages neural networks to estimate sea states from onboard WEC measurements such as position, velocity, and force. Using a point absorber WEC device as a test platform, our proposed approach estimates sea states in real-time and subsequently adjusts PID controller gains to maximize energy extraction. Simulation results across diverse sea conditions demonstrate that our strategy eliminates the need for external wave monitoring equipment while maintaining power capture efficiency. The results show that our neural network-based control technique can improve power capture by 25.6% while significantly reducing system complexity. This approach offers a practical alternative for WEC deployments where direct wave measurements are either infeasible or cost prohibitive.

PIDcontrol↗
NASA NTRS1977

Dynamic modeling and sensitivity analysis of solar thermal energy conversion systems

Since the energy input to solar thermal conversion systems is both time variant and probabilistic, it is unlikely that simple steady-state methods for estimating lifetime performance will provide satisfactory results. The work described here uses dynamic modeling to begin identifying what must be known about input radiation and system dynamic characteristics to estimate performance reliably. Daily operation of two conceptual solar energy systems was simulated under varying operating strategies with time-dependent radiation intensity ranging from smooth input of several magnitudes to input of constant total energy whose intensity oscillated with periods from 1/4 hour to 6 hours. Integrated daily system output and efficiency were functions of both level and dynamic characteristics of insolation. Sensitivity of output to changes in total input was greater than one.

Hamilton, C. L.↗
NASA NTRS2000

On-Board Real-Time State and Fault Identification for Rovers

For extended autonomous operation, rovers must identify potential faults to determine whether its execution needs to be halted or not. At the same time, rovers present particular challenges for state estimation techniques: they are subject to environmental influences that affect senior readings during normal and anomalous operation, and the sensors fluctuate rapidly both because of noise and because of the dynamics of the rover's interaction with its environment. This paper presents MAKSI, an on-board method for state estimation and fault diagnosis that is particularly appropriate for rovers. The method is based on a combination of continuous state estimation, wing Kalman filters, and discrete state estimation, wing a Markov-model representation.

Washington, Richard↗
NASA NTRS2011

Evaluating the Application of Multi-Satellite Observations in Hydrologic Modeling

When monitoring local or regional hydrosphere dynamics for applications such as agricultural productivity or drought and flooding events, it is necessary to have accurate, high-resolution estimates of terrestrial water and energy storages. Though in-situ observations provide reliable estimates of hydrologic states and fluxes, they are only capable of accurately capturing the dynamics at relatively discrete points in space and time, which makes them inadequate for characterizing the variability of the water budget across scales. In contrast, satellite-based remote sensing is ideal for providing observations of hydrological states and fluxes because it provides spatially-distributed observations at spatial and temporal scales required for regional land surface process modeling. Due to the continued progress in algorithm development and emerging satellite technology, we now have near-real time monitoring of several components of the water cycle including precipitation, soil moisture, lake and river height, terrestrial water storage, snow cover, and evapotranspiration. As these data become more readily available, their application to hydrologic modeling is becoming more common, however there remains little consensus on the most appropriate method for optimal integration and evaluation in regard to hydrological applications. Here we present two case studies operationally applying several remotely sensed products from AMSR-E, GRACE, and MODIS and discuss assimilation strategies, ease of integration and interpretation, and methods for quantifying the success of the application methodology.

Bolten, John↗
NASA NTRS1973

Results of solar electric thrust vector control system design, development and tests

Efforts to develop and test a thrust vector control system TVCS for a solar-energy-powered ion engine array are described. The results of solar electric propulsion system technology (SEPST) III real-time tests of present versions of TVCS hardware in combination with computer-simulated attitude dynamics of a solar electric multi-mission spacecraft (SEMMS) Phase A-type spacecraft configuration are summarized. Work on an improved solar electric TVCS, based on the use of a state estimator, is described. SEPST III tests of TVCS hardware have generally proved successful and dynamic response of the system is close to predictions. It appears that, if TVCS electronic hardware can be effectively replaced by control computer software, a significant advantage in control capability and flexibility can be gained in future developmental testing, with practical implications for flight systems as well. Finally, it is concluded from computer simulations that TVCS stabilization using rate estimation promises a substantial performance improvement over the present design.

Fleischer, G. E.↗
NASA NTRS1981

Parameter estimation of large flexible aerospace structures with application to the control of the Maypole Deployable Reflector

Systems such as the Maypole deployable reflector have a distributed parameter nature. The flexible column and hoop structure and the circular antenna of 30-100 meter diameter which it supports are described by partial, rather than ordinary, differential equations. Progress completed in reduced order modelling andd controller design and digital parameter estimation and control is summarized. Topics covered include depolyment and on-orbit operation; quasi-static (steady state) operation; dynamic distributed parameter system; autoregressive moving average identification; frequency domain procedures; direct or implicit active control; adaptive observers; parameter estimation using a linear reinforcement learning factor; feedback control; and reduced order modeling for nonlinear systems.

Balas, M. J.↗
DOE OSTI2013

Assimilating irregularly spaced sparsely observed turbulent signals with hierarchical Bayesian reduced stochastic filters

In this paper, we consider a practical filtering approach for assimilating irregularly spaced, sparsely observed turbulent signals through a hierarchical Bayesian reduced stochastic filtering framework. The proposed hierarchical Bayesian approach consists of two steps, blending a data-driven interpolation scheme and the Mean Stochastic Model (MSM) filter. We examine the potential of using the deterministic piecewise linear interpolation scheme and the ordinary kriging scheme in interpolating irregularly spaced raw data to regularly spaced processed data and the importance of dynamical constraint (through MSM) in filtering the processed data on a numerically stiff state estimation problem. In particular, we test this approach on a two-layer quasi-geostrophic model in a two-dimensional domain with a small radius of deformation to mimic ocean turbulence. Our numerical results suggest that the dynamical constraint becomes important when the observation noise variance is large. Second, we find that the filtered estimates with ordinary kriging are superior to those with linear interpolation when observation networks are not too sparse; such robust results are found from numerical simulations with many randomly simulated irregularly spaced observation networks, various observation time intervals, and observation error variances. Third, when the observation network is very sparse, we find that both the kriging and linear interpolations are comparable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Nuclear quantum effects on adsorption of H 2 and isotopologues on metal ions

The nuclear quantum effects on the zero-point energy (ZPE), influencing adsorption of H$_2$ and isotopologues on metal ions, are examined in this study using normal mode analysis of ab initio electronic structure results for complexes with 17 metal cations. To estimate for the anharmonicity, a nuclear wavepacket dynamics on the ground state electronic potential energy surfaces (PES) have been employed for complexes of Li$^+$ and Cu$^{+2}$ with H$_2$, D$_2$, HD. The dynamics analysis shows that incorporation of the PES anharmonicity changes the ZPE by up to 9%. Finally, the lightest metallic nuclei, Li and Be, are found to be the most 'quantum'. The largest selectivity in adsorption is predicted for Cu, Ni and Co ions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Order conditions for nonlinearly partitioned Runge-Kutta methods

Recently, a new class of nonlinearly partitioned Runge–Kutta (NPRK) methods was proposed for nonlinearly partitioned systems of autonomous ordinary differential equations y' = F(y, y). The target class of problems are those in which different scales, stiffnesses, or physics are coupled in a nonlinear way, wherein the desired partition cannot be written in a classical additive or component-wise fashion. Here we use a rooted-tree analysis to derive full-order conditions for NPRKM methods, where M denotes the number of nonlinear partitions. Due to the nonlinear coupling and thereby the mixed product differentials, it turns out that the standard node-colored rooted tree analysis used in analyzing ODE integrators does not naturally apply. Instead we develop a new edge-colored rooted-tree framework to address the nonlinear coupling. The resulting order conditions are enumerated, are provided directly for up to fourth order with M = 2 and third order with M = 3, and are related to existing order conditions of additive and partitioned RK methods. We conclude with an example that shows how the nonlinear order conditions can be used to obtain an embedded estimate of the state-dependent nonlinear coupling strength in a dynamical system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Linear minimum variance filters applied to carrier tracking

A new approach is taken to the problem of tracking a fixed amplitude signal with a Brownian-motion phase process. Classically, a first-order phase-lock loop (PLL) is used; here, the problem is treated via estimation of the quadrature signal components. In this space, the state dynamics are linear with white multiplicative noise. Therefore, linear minimum-variance filters, which have a particularly simple mechanization, are suggested. The resulting error dynamics are linear at any signal/noise ratio, unlike the classical PLL. During synchronization, and above threshold, this filter with constant gains degrades by 3 per cent in output rms phase error with respect to the classical loop. However, up to 80 per cent of the maximum possible noise improvement is obtained below threshold, where the classical loop is nonoptimum, as demonstrated by a Monte Carlo analysis. Filter mechanizations are presented for both carrier and baseband operation.

Gustafson, D. E.↗
NASA NTRS1986

Optical processing and Space Station automation

Optical information processing research aimed at Space Station automation applications is reviewed. The emphasis of the NASA Ames Research Center program is on intelligent optical pattern recognition and optical control processing. Attention is given to the primary functions of an overall scene understanding system: distortion-invariant optical feature generation, dimensionality reduction, object classification, and contextual information processing. A method of using synthetic discriminant functions to facilitate learning in a high-speed optical correlator is described. A discussion is presented of candidate analog and digital architectures for the optical implementation of state-estimation algorithms needed for the control of high-dimension dynamic systems. The multivariate system chosen for the optical control technology demonstration - a segmented, adaptive mirror and interferometrically based wavefront sensor - is also described.

Ennis, D. J.↗