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NASA NTRS2011

On Using Exponential Parameter Estimators with an Adaptive Controller

Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.

Patre, Parag↗

NASA NTRSDate not supplied

Combining Earth System Modeling and Machine Learning to Investigate Volcanic Sulfate Deposition in Polar Ice Cores

Volcanic eruptions emit large amounts of sulfur dioxide (SO2), water, and other chemicals into the atmosphere, both in the troposphere and the stratosphere. Most of the SO2 is converted to sulfate aerosol, which is eventually deposited following long-range transport. The deposits from large eruptions are potentially detectable in ice cores, but there are many cases in which sulfate layers have not been linked to their source volcanoes. As volcanoes can act as significant shocks to the global climate system, we are interested in locating these eruptions in order to increase understanding of the volcanic record. To narrow down the search, we performed 140 simulations of volcanic eruptions using the GISS ModelE Earth system model. We varied the latitude, longitude, Julian day, plume top, plume bottom, and injected SO2 and H2O amounts using a Latin hypercube sampling approach, and analyzed correlations between these parameters and sulfate depositions at ice core sites in Antarctica and Greenland. Using machine learning and parameter estimation, we generated probability distributions and maximum likelihood estimates for the parameters given sulfate deposition data, which can predict latitude with some skill. We find that the volcano latitude and SO2 content are best correlated with sulfate depositions at each pole, while longitude, Julian day, and H2O have small or insignificant effects. Plume altitude and thickness are important because they determine how much of the SO2 is injected into the stratosphere, which has implications for sulfur transport and lifetimes.

Earth system models↗

NASA NTRS1992

A combined surface/volume scattering retracking algorithm for ice sheet satellite altimetry

An algorithm that is based upon a combined surface-volume scattering model is developed. It can be used to retrack individual altimeter waveforms over ice sheets. An iterative least-squares procedure is used to fit the combined model to the return waveforms. The retracking algorithm comprises two distinct sections. The first generates initial model parameter estimates from a filtered altimeter waveform. The second uses the initial estimates, the theoretical model, and the waveform data to generate corrected parameter estimates. This retracking algorithm can be used to assess the accuracy of elevations produced from current retracking algorithms when subsurface volume scattering is present. This is extremely important so that repeated altimeter elevation measurements can be used to accurately detect changes in the mass balance of the ice sheets. By analyzing the distribution of the model parameters over large portions of the ice sheet, regional and seasonal variations in the near-surface properties of the snowpack can be quantified.

Davis, Curt H.↗

NASA NTRS2008

Controller for Driving a Piezoelectric Actuator at Resonance

A digital control system based partly on an extremum-seeking control algorithm tracks the changing resonance frequency of a piezoelectric actuator or an electrically similar electromechanical device that is driven by a sinusoidal excitation signal and is required to be maintained at or near resonance in the presence of uncertain, changing external loads and disturbances. Somewhat more specifically, on the basis of measurements of the performance of the actuator, this system repeatedly estimates the resonance frequency and alters the excitation frequency as needed to keep it at or near the resonance frequency. In the original application for which this controller was developed, the piezoelectric actuator is part of an ultrasonic/sonic drill/corer. Going beyond this application, the underlying principles of design and operation are generally applicable to tracking changing resonance frequencies of heavily perturbed harmonic oscillators. Resonance-frequency-tracking analog electronic circuits are commercially available, but are not adequate for the present purpose for several reasons: The input/output characteristics of analog circuits tend to drift, often necessitating recalibration, especially whenever the same controller is used in driving a different resonator. In the case of an actuator in a system that has multiple modes characterized by different resonance frequencies, an analog controller can tune erroneously to one of the higher-frequency modes. The lack of programmability of analog controllers is problematic when faults occur, and is especially problematic for preventing tuning to a higher-frequency mode. In contrast, a digital controller can be programmed to restrict itself to a specified frequency range and to maintain stability even when the affected resonator is driven at high power and subjected to uncertain disturbances and variable loads. The present digital control system (see figure) is implemented by means of an algorithm that comprises three main subalgorithms: a hill-climbing control algorithm, an estimation-based extremum-seeking control (ESC) algorithm, and a supervisory algorithm. The hill-climbing algorithm is useful for coarse tracking to find and remain within the vicinity of the resonance. The ESC algorithm is not capable of coarse resonance tracking, but is capable of fine resonance tracking once the estimates of parameters generated by the hill-climbing algorithm have converged sufficiently. On the basis of the parameter-estimation errors, the supervisory algorithm switches operation to whichever of the other two algorithms performs best at a given time.

Aldrich, Jack↗

NASA NTRS1972

On the adaptive control of linear systems using the open-loop-feedback-optimal approach.

This paper considers the suboptimal stochastic control of linear discrete-time dynamical systems with unknown or stochastically varying parameters. The suboptimal scheme is based upon the use of the open-loop-feedback-optimal (O.L.F.O.) method. The state and parameter estimates are generated by an extended Kalman filter algorithm. Numerical results for first order systems are presented.

Ku, R.↗

NASA NTRS1973

On the adaptive control of linear systems using the open-loop-feedback-optimal approach.

This paper considers the suboptimal stochastic control of linear discrete-time dynamical systems with uncertain or stochastically varying parameters. The suboptimal scheme is based upon the use of the open-loop-feedback-optimal (OLFO) method. The state and parameter estimates are generated by an extended Kalman filter algorithm. Numerical results for first-order systems are presented.

Ku, R.↗

NASA NTRS1973

Open-loop-feedback-optimal adaptive stochastic control of linear systems

This paper considers the suboptimal stochastic control of linear discrete-time dynamical systems with unknown or stochastically varying parameters. The suboptimal scheme is based upon the use of the open-loop-feedback-optimal method. The state and parameter estimates are generated by an extended Kalman filter algorithm. Numerical results for first-order systems are presented.

Ku, R.↗

NASA NTRS1983

Space station needs, attributes and architectural options study costing working group briefing

The interface between tasks and the sequence of information flow are charted. The mission model and the requirements of each mission. These requirements along with the mission model schedule resulted in a further definition of the mission payloads, the Space Station architecture and the STS requirements was developed in task 2. The information was used to estimate the cost parameters necessary to generate cost estimates for the system with the aid of cost models for the Missions, Space Station and Space Transportation.

Source record↗

NASA NTRS1993

RENEW v3.2 user's manual, maintenance estimation simulation for Space Station Freedom Program

RENEW is a maintenance event estimation simulation program developed in support of the Space Station Freedom Program (SSFP). This simulation uses reliability and maintainability (R&M) and logistics data to estimate both average and time dependent maintenance demands. The simulation uses Monte Carlo techniques to generate failure and repair times as a function of the R&M and logistics parameters. The estimates are generated for a single type of orbital replacement unit (ORU). The simulation has been in use by the SSFP Work Package 4 prime contractor, Rocketdyne, since January 1991. The RENEW simulation gives closer estimates of performance since it uses a time dependent approach and depicts more factors affecting ORU failure and repair than steady state average calculations. RENEW gives both average and time dependent demand values. Graphs of failures over the mission period and yearly failure occurrences are generated. The averages demand rate for the ORU over the mission period is also calculated. While RENEW displays the results in graphs, the results are also available in a data file for further use by spreadsheets or other programs. The process of using RENEW starts with keyboard entry of the R&M and operational data. Once entered, the data may be saved in a data file for later retrieval. The parameters may be viewed and changed after entry using RENEW. The simulation program runs the number of Monte Carlo simulations requested by the operator. Plots and tables of the results can be viewed on the screen or sent to a printer. The results of the simulation are saved along with the input data. Help screens are provided with each menu and data entry screen.

Bream, Bruce L.↗

NASA NTRS1985

A Residuals Approach to Filtering, Smoothing and Identification for Static Distributed Systems

An approach for state estimation and identification of spatially distributed parameters embedded in static distributed (elliptic) system models is advanced. The method of maximum likelihood is used to find parameter values that maximize a likelihood functional for the system model, or equivalently, that minimize the negative logarithm of this functional. To find the minimum, a Newton-Raphson search is conducted that from an initial estimate generates a convergent sequence of parameter estimates. For simplicity, a Gauss-Markov approach is used to approximate the Hessian in terms of products of first derivatives. The gradient and approximate Hessian are computed by first arranging the negative log likelihood functional into a form based on the square root factorization of the predicted covariance of the measurement process. The resulting data processing approach, referred to here by the new term of predicted data covariance square root filtering, makes the gradient and approximate Hessian calculations very simple. A closely related set of state estimates is also produced by the maximum likelihood method: smoothed estimates that are optimal in a conditional mean sense and filtered estimates that emerge from the predicted data covariance square root filter.

Rodriguez, G.↗

NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗

NASA NTRS2024

Modeling Entanglement-Based Quantum Key Distribution for the NASA Quantum Communications Analysis Suite

One of the most practical, and sought after, applications of quantum mechanics in the field of information science is the use of entanglement distribution to communicate quantum information effectively. Similar to the continued improvements of functional quantum computers over the past decade, advances in demonstrations of entanglement distribution over long distances may enable new applications in aeronautics and space communications. The existing NASA Quantum Communications Analysis Suite (NQCAS) software models such applications, but limited experimental data exists to verify the model’s theoretical results. There is, however, a large body of experimental data in the relevant literature for entanglement-based quantum key distribution (QKD). This paper details a Monte Carlo based QKD model that uses NQCAS input parameters to generate an estimated QKD link budget for verification of NQCAS. The model generates link budget statistics like key rates, error rates, and S values that can then be compared to the experimental values in the literature. Preliminary comparisons show many similarities between the simulated and experimental data, supporting the model’s validity. A verified NQCAS model will inform experimental work conducted in Glenn Research Center’s (GRC) NASA Quantum Metrology Laboratory (NQML), supporting the United States Quantum Initiative and potential NASA missions.

NASA Quantum Communications Analysis Suite↗

NASA NTRS1982

An identification algorithm for linear stochastic systems with time delays

Linear discrete stochastic control systems containing unknown multiple time delays, plant parameters and noise variances are considered. An algorithm is established which uses the maximum-likelihood technique to identify the unknown parameters. An estimated likelihood function is evaluated based on the previous parameter estimates, which in turn generates a new descent direction vector to update the unknown parameters. The delays and plant parameters are identified in their respective parameter spaces. An example of a second-order stochastic system has been implemented by digital simulation to demonstrate the applicability of the algorithm.

Leondes, C. T.↗

NASA NTRS2011

Lateral-Directional Parameter Estimation on the X-48B Aircraft Using an Abstracted, Multi-Objective Effector Model

The problem of parameter estimation on hybrid-wing-body aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aerodynamic control effectors that act in coplanar motion. This adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of flight and simulation data must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, time-decorrelation techniques are applied to a model structure selected through stepwise regression for simulated and flight-generated lateral-directional parameter estimation data. A virtual effector model that uses mathematical abstractions to describe the multi-axis effects of clamshell surfaces is developed and applied. Comparisons are made between time history reconstructions and observed data in order to assess the accuracy of the regression model. The Cram r-Rao lower bounds of the estimated parameters are used to assess the uncertainty of the regression model relative to alternative models. Stepwise regression was found to be a useful technique for lateral-directional model design for hybrid-wing-body aircraft, as suggested by available flight data. Based on the results of this study, linear regression parameter estimation methods using abstracted effectors are expected to perform well for hybrid-wing-body aircraft properly equipped for the task.

Ratnayake, Nalin A.↗

NASA NTRS1991

NASA, Navy, and AES/York sea ice concentration comparison of SSM/I algorithms with SAR derived values

Previous research studies have focused on producing algorithms for extracting geophysical information from passive microwave data regarding ice floe size, sea ice concentration, open water lead locations, and sea ice extent. These studies have resulted in four separate algorithms for extracting these geophysical parameters. Sea ice concentration estimates generated from each of these algorithms (i.e., NASA/Team, NASA/Comiso, AES/York, and Navy) are compared to ice concentration estimates produced from coincident high-resolution synthetic aperture radar (SAR) data. The SAR concentration estimates are produced from data collected in both the Beaufort Sea and the Greenland Sea in March 1988 and March 1989, respectively. The SAR data are coincident to the passive microwave data generated by the Special Sensor Microwave/Imager (SSM/I).

Jentz, R. R.↗

NASA NTRS2010

Onboard Processing of Multispectral and Hyperspectral Data of Volcanic Activity for Future Earth-Orbiting and Planetary Missions

Autonomous onboard processing of data allows rapid response to detections of dynamic, changing processes. Software that can detect volcanic eruptions from thermal emission has been used to retask the Earth Observing 1 spacecraft to obtain additional data of the eruption. Rapid transmission of these data to the ground, and the automatic processing of the data to generated images, estimates of eruption parameters and maps of thermal structure, has allowed these products to be delivered rapidly to volcanologists to aid them in assessing eruption risk and hazard. Such applications will enhance science return from future Earth-orbiting spacecraft and also from spacecraft exploring the Solar System, or beyond, which hope to image dynamic processes. Especially in the latter case, long communication times between the spacecraft and Earth exclude a rapid response to what may be a transient process - only using onboard autonomy can the spacecraft react quickly to such an event.

Jupiter↗

NASA NTRS2012

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

Barlow, Jonathan↗

NASA NTRS2011

A Minimum (Delta)V Orbit Maintenance Strategy for Low-Altitude Missions Using Burn Parameter Optimization

Orbit maintenance is the series of burns performed during a mission to ensure the orbit satisfies mission constraints. Low-altitude missions often require non-trivial orbit maintenance (Delta)V due to sizable orbital perturbations and minimum altitude thresholds. A strategy is presented for minimizing this (Delta)V using impulsive burn parameter optimization. An initial estimate for the burn parameters is generated by considering a feasible solution to the orbit maintenance problem. An example demonstrates the dV savings from the feasible solution to the optimal solution.

Brown, Aaron J.↗