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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Second-order state estimation experiments using acceleration measurements

The estimation of dynamic states for feedback control of structural systems using second-order differential equations and acceleration measurements is described. The formulation of the observer model, and the design of the observer gains is discussed in detail. It is shown the second-order observer is highly stable because the stability constraints on the observer gains are model independent. The limitation of the proposed observer is the need for 'nearly' collocated actuators and accelerometers. Experimental results using a control-structure interaction testbed are presented that show the second-order observer provided more stability than a Kalman filter estimator without decreasing closed-loop performance.

Belvin, W. K.↗
DOE OSTI2013

SPIN STATE AND MOMENT OF INERTIA CHARACTERIZATION OF 4179 TOUTATIS

The 4.5 km long near-Earth asteroid 4179 Toutatis has made close Earth flybys approximately every four years between 1992 and 2012, and has been observed with high-resolution radar imaging during each approach. Its most recent Earth flyby in 2012 December was observed extensively at the Goldstone and Very Large Array radar telescopes. In this paper, Toutatis' spin state dynamics are estimated from observations of five flybys between 1992 and 2008. Observations were used to fit Toutatis' spin state dynamics in a least-squares sense, with the solar and terrestrial tidal torques incorporated in the dynamical model. The estimated parameters are Toutatis' Euler angles, angular velocity, moments of inertia, and the center-of-mass-center-of-figure offset. The spin state dynamics as well as the uncertainties of the Euler angles and angular velocity of the converged solution are then propagated to 2012 December in order to compare the dynamical model to the most recent Toutatis observations. The same technique of rotational dynamics estimation can be applied to any other tumbling body, given sufficiently accurate observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation

As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However in practice the process and measurement noise models are usually difficult to obtain. Thus we have developed the Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.

Zhang, Jinghe↗
DOE OSTI2022

Dynamic, resilient virtual sensing system and shadow controller for cyber-attack neutralization

An industrial asset may have monitoring nodes (e.g., sensor or actuator nodes) that generate current monitoring node values. An abnormality detection and localization computer may receive the series of current monitoring node values and output an indication of at least one abnormal monitoring node that is currently being attacked or experiencing a fault. An actor-critic platform may tune a dynamic, resilient state estimator for a sensor node and output tuning parameters for a controller that improve operation of the industrial asset during the current attack or fault. The actor-critic platform may include, for example, a dynamic, resilient state estimator, an actor model, and a critic model. According to some embodiments, a value function of the critic model is updated for each action of the actor model and each action of the actor model is evaluated by the critic model to update a policy of the actor-critic platform.

Roychowdhury, Subhrajit↗
DOE OSTI2017

On state estimation and fusion with elliptical constraints

We consider tracking of a target with elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the known ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to the center, and (ii) shortest distance to the ellipse are discussed. A tracking example is used to illustrate the tracking performance using projection-based methods with various fusers in the lossy long-haul tracking environment.

Rao, Nageswara S.↗
DOE OSTI2018

Performance of State Estimation and Fusion with Elliptical Motion Constraints

We consider tracking of a target with known elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to the center, and (ii) shortest distance to the ellipse are discussed. A tracking example is used to illustrate the tracking performance using projection-based methods with various fusers in a lossy long-haul tracking environment.

Liu, Qiang↗
NASA NTRS2022

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗
DOE OSTI2013

An Expectation-Maximization Method for Calibrating Synchronous Machine Models

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using EM method are also presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

EM Algorithms↗
NASA NTRS1988

Optimal post-experiment estimation of poorly modeled dynamic systems

Recently, a novel strategy for post-experiment state estimation of discretely-measured dynamic systems has been developed. The method accounts for errors in the system dynamic model equations in a more general and rigorous manner than do filter-smoother algorithms. The dynamic model error terms do not require the usual process noise assumptions of zero-mean, symmetrically distributed random disturbances. Instead, the model error terms require no prior assumptions other than piecewise continuity. The resulting state estimates are more accurate than filters for applications in which the dynamic model error clearly violates the typical process noise assumptions, and the available measurements are sparse and/or noisy. Estimates of the dynamic model error, in addition to the states, are obtained as part of the solution of a two-point boundary value problem, and may be exploited for numerous reasons. In this paper, the basic technique is explained, and several example applications are given. Included among the examples are both state estimation and exploitation of the model error estimates.

Mook, D. Joseph↗
DOE OSTI2020

Current Observer Based Predictive Decoupled Power Control Grid-Interactive Inverter

This paper presents a sensor-less current model predictive control (MPC) scheme via a full state observer based current estimator. The grid interactive inverters’ control schemes require current and voltage sensors. Elimination of current sensor enhances the inverter reliability. This paper leverages the inherent characteristics of MPC towards robust current sensor-less grid interactive inverter with LC filter. The observer for inductor current is developed based on the existing capacitor voltage measurement. The estimator dynamic state-space matrices are then obtained through reconstruction of the inverter model with the capacitor voltage and inductor current being the state variables. The controller objectives are to regulate active and reactive power in a decoupled manner. The theoretical expectation, controller performance, and accuracy of the current estimation are verified by conducting a real-time simulation via Typhoon HIL.

Zhang, Zhen↗
DOE OSTI2013

THE CONTRIBUTION OF HALOS WITH DIFFERENT MASS RATIOS TO THE OVERALL GROWTH OF CLUSTER-SIZED HALOS

We provide a new observational test for a key prediction of the ΛCDM cosmological model: the contributions of mergers with different halo-to-main-cluster mass ratios to cluster-sized halo growth. We perform this test by dynamically analyzing 7 galaxy clusters, spanning the redshift range 0.13 < z{sub c} < 0.45 and caustic mass range 0.4-1.5 10{sup 15} h{sub 0.73}{sup -1} M{sub ☉}, with an average of 293 spectroscopically confirmed bound galaxies to each cluster. The large radial coverage (a few virial radii), which covers the whole infall region, with a high number of spectroscopically identified galaxies enables this new study. For each cluster, we identify bound galaxies. Out of these galaxies, we identify infalling and accreted halos and estimate their masses and their dynamical states. Using the estimated masses, we derive the contribution of different mass ratios to cluster-sized halo growth. For mass ratios between ∼0.2 and ∼0.7, we find a ∼1σ agreement with ΛCDM expectations based on the Millennium simulations I and II. At low mass ratios, ∼< 0.2, our derived contribution is underestimated since the detection efficiency decreases at low masses, ∼2 × 10{sup 14} h{sub 0.73}{sup -1} M{sub ☉}. At large mass ratios, ∼> 0.7, we do not detect halos probably because our sample, which was chosen to be quite X-ray relaxed, is biased against large mass ratios. Therefore, at large mass ratios, the derived contribution is also underestimated.

79 ASTRONOMY AND ASTROPHYSICS↗