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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Erratum: Weighing the giants – V. Galaxy cluster scaling relations

We present constraints on the scaling relations of galaxy cluster X-ray luminosity, temperature and gas mass (and derived quantities) with mass and redshift, employing masses from robust weak gravitational lensing measurements. These are the first such results obtained from an analysis that simultaneously accounts for selection effects and the underlying mass function, and directly incorporates lensing data to constrain total masses. Our constraints on the scaling relations and their intrinsic scatters are in good agreement with previous studies, and reinforce a picture in which departures from self-similar scaling laws are primarily limited to cluster cores. However, the data are beginning to reveal new features that have implications for cluster astrophysics and provide new tests for hydrodynamical simulations. We find a positive correlation in the intrinsic scatters of luminosity and temperature at fixed mass, which is related to the dynamical state of the clusters. While the evolution of the nominal scaling relations over the redshift range 0.0 < z < 0.5 is consistent with self similarity, we find tentative evidence that the luminosity and temperature scatters respectively decrease and increase with redshift. Physically, this likely related to the development of cool cores and the rate of major mergers. We also examine the scaling relations of redMaPPer richness and Compton Y from Planck . While the richness{mass relation is in excellent agreement with recent work, the measured Y {mass relation departs strongly from that assumed in the Planck cluster cosmology analysis. Furthermore, the latter result is consistent with earlier comparisons of lensing and Planck scaling-relation-derived masses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Direct Visualization of Surface Spin-Flip Transition in MnBi 4 Te 7

Here, we report direct visualization of spin-flip transition of the surface layer in antiferromagnet MnBi 4 Te 7 , a natural superlattice of alternating MnBi 2 Te 4 and Bi 2 Te 3 layers, using cryogenic magnetic force microscopy (MFM). The observation of magnetic contrast across domain walls and step edges confirms that the antiferromagnetic order persists to the surface layers. The magnetic field dependence of the MFM images reveals that the surface magnetic layer undergoes a first-order spin-flip transition at a magnetic field that is lower than the bulk transition, in excellent agreement with a revised Mills model. Our analysis suggests no reduction of the order parameter in the surface magnetic layer, implying robust ferromagnetism in the single-layer limit. The direct visualization of surface spin-flip transition not only opens up exploration of surface metamagnetic transitions in layered antiferromagnets, but also provides experimental support for realizing quantized transport in ultrathin films of MnBi 4 Te 7 and other natural superlattice topological magnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Improving neutrino oscillation measurements through event classification

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance–information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasielastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE 𝜈 𝜇 disappearance analysis, yields improved accuracy and sensitivity at the 10%–20% level. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements.

Ellis, Sebastian A. R. [King's College, London (Un↗
DOE OSTI2016

SHARP: a distributed GPU-based ptychographic solver

Ever brighter light sources, fast parallel detectors and advances in phase retrieval methods have made ptychography a practical and popular imaging technique. Compared to previous techniques, ptychography provides superior robustness and resolution at the expense of more advanced and time-consuming data analysis. Therefore, by taking advantage of massively parallel architectures, high-throughput processing can expedite this analysis and provide microscopists with immediate feedback. These advances allow real-time imaging at wavelength-limited resolution, coupled with a large field of view. This article describes a set of algorithmic and computational methodologies used at the Advanced Light Source and US Department of Energy light sources. These are packaged as a CUDA-based software environment named SHARP, aimed at providing state-of-the-art high-throughput ptychography reconstructions for the coming era of diffraction-limited light sources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

SU-C-304-04: A Compact Modular Computational Platform for Automated On-Board Imager Quality Assurance

Purpose: Traditionally, the assessment of X-ray tube output and detector positioning accuracy of on-board imagers (OBI) has been performed manually and subjectively with rulers and dosimeters, and typically takes hours to complete. In this study, we have designed a compact modular computational platform to automatically analyze OBI images acquired with in-house designed phantoms as an efficient and robust surrogate. Methods: The platform was developed as an integrated and automated image analysis-based platform using MATLAB for easy modification and maintenance. Given a set of images acquired with the in-house designed phantoms, the X-ray output accuracy was examined via cross-validation of the uniqueness and integration minimization of important image quality assessment metrics, while machine geometric and positioning accuracy were validated by utilizing pattern-recognition based image analysis techniques. Results: The platform input was a set of images of an in-house designed phantom. The total processing time is about 1–2 minutes. Based on the data acquired from three Varian Truebeam machines over the course of 3 months, the designed test validation strategy achieved higher accuracy than traditional methods. The kVp output accuracy can be verified within +/−2 kVp, the exposure accuracy within 2%, and exposure linearity with a coefficient of variation (CV) of 0.1. Sub-millimeter position accuracy was achieved for the lateral and longitudinal positioning tests, while vertical positioning accuracy within +/−2 mm was achieved. Conclusion: This new platform delivers to the radiotherapy field an automated, efficient, and stable image analysis-based procedure, for the first time, acting as a surrogate for traditional tests for LINAC OBI systems. It has great potential to facilitate OBI quality assurance (QA) with the assistance of advanced image processing techniques. In addition, it provides flexible integration of additional tests for expediting other OBI quality assurance tests, such as 2D/3D image quality, making completely automated QA possible. Research Funding from Varian Medical Systems Inc. . Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Energy gap evolution across the superconductivity dome in single crystals of (Ba 1-x K x )Fe 2 As 2

The mechanism of unconventional superconductivity in iron-based superconductors (IBSs) is one of the most intriguing questions in current materials research. Among non-oxide IBSs, (Ba 1$-$x K x )Fe 2 As 2 has been intensively studied because of its high superconducting transition temperature and fascinating evolution of the superconducting gap structure from being fully isotropic at optimal doping (x ≈ 0.4) to becoming nodal at x > 0.8. Although this marked evolution was identified in several independent experiments, there are no details of the gap evolution to date because of the lack of high-quality single crystals covering the entire K-doping range of the superconducting dome. In this work, we conducted a systematic study of the London penetration depth, λ(T), across the full phase diagram for different concentrations of point-like defects introduced by 2.5-MeV electron irradiation. Fitting the low-temperature variation with the power law, Δλ ~ T n , we find that the exponent n is the highest and the Tc suppression rate with disorder is the smallest at optimal doping, and they evolve with doping being away from optimal, which is consistent with increasing gap anisotropy, including an abrupt change around x ≃ 0.8, indicating the onset of nodal behavior. Our analysis using a self-consistent t-matrix approach suggests the ubiquitous and robust nature of s ± pairing in IBSs and argues against a previously suggested transition to a d-wave state near x = 1 in this system.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Python GST Implementation (PyGSTi) v. 0.9

PyGSTi is a Python package for modeling and characterizing noise (errors) in small quantum information processors. In addition to being a basic framework for describing quantum circuits and noise models, it implements mainstream quantum characterization, verification, and validation (QCVV) protocols such as Gate Set Tomography (GST), Randomized Benchmarking (RB), Robust Phase Estimation, and Idle Tomography. It also implements prototype protocols used for timeseries analysis and crosstalk detection, all of which have the goal of better understanding the noise found in existing as-built experimental devices. The central protocol of pyGSTi (from where it derives its name) is Gate Set Tomography. GST is a theory and protocol for simultaneously estimating the state preparation, gate operations, and measurement effects of a physical system of one or many quantum bits (qubits). These estimates are based entirely on the statistics of experimental measurements, and their interpretation and analysis can provide a detailed understanding of the types of errors/imperfections in the physical system. In this way, GST provides not only a means of certifying the “goodness” of qubits but also a means of debugging (i.e. improving) them. The other protocols follow this similar pattern in that they use statistical inference and analysis of experimental data to estimate one or more properties of the noise in a device.

Nielsen, Erik↗
DOE OSTI2024

msdlive-cli-distro

MSD-LIVE, the MultiSector Dynamics – Living, Intuitive, Value-adding, Environment, is a flexible and scalable data and code management system combined with a distributed computational platform that will enable MSD researchers to document and archive their data, run their models and analysis tools, and share their data, software, and multi-model workflows within a robust Community of Practice. MSD-LIVE will facilitate a new open, collaborative, resource-rich, technology-facilitated, community-driven way of doing MSD research.

Lansing, Carina↗
DOE OSTI2023

PacWave South SeaRAY k2 Risk Registers

The SeaRAY is a deployable power system for maritime sensors, monitoring equipment, communications, unmanned underwater vehicles, and other similar payloads. This project is to design, deliver, and test a prototype low-power WEC that lowers the total cost of ownership and provides robust, new capabilities for customers in the maritime environment. Failure Modes, Effects, and Criticality Analysis (FMECA) is conducted to systematically identify all potential failure modes and their effects on the system, and to analyze the criticality of each risk based on the likelihood of the event and the severity of the impact. Actions may then be recommended to mitigate the criticality of a risk, either by reducing the likelihood of the risk or the severity of its impact. Risk assessment is executed iteratively as an integral part of the design process. By incorporating risk assessment early in the development cycle, mitigation of risk can be achieved cost effectively. The actions recommended to mitigate risk may be subsequently executed, and as the design progresses the risk assessment is reviewed and revised. Review of the risk assessment is integrated into structured design reviews, ensuring that critical risks are comprehended and that the Project will not progress to e.g. fabrication while intolerable risks remain. The risk assessment process results in the population and maintenance of Risk Registers (RRs). Each major system (and as needed, subsystem) will have a distinct RR. This allows each system or subsystem to be assessed individually, rendering the RRs to a manageable size for review.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Development of Smart Ventilation Control Algorithms for Humidity Control in High-Performance Homes in Humid U.S. Climates

Past field research and simulation studies have shown that high performance homes experience elevated indoor humidity levels for substantial portions of the year in humid climates. This is largely the result of lower sensible cooling loads, which reduces the moisture removed by the cooling system. These elevated humidity levels lead to concerns about occupant comfort, health and building durability. Use of mechanical ventilation at rates specified in ASHRAE Standard 62.2-2013 are often cited as an additional contributor to humidity problems in these homes. Past research has explored solutions, including supplemental dehumidification, cooling system operational enhancements and ventilation system design (e.g., ERV, supply, exhaust, etc.). This project’s goal is to develop and demonstrate (through simulations) smart ventilation strategies that can contribute to humidity control in high performance homes. These strategies must maintain IAQ via equivalence with ASHRAE Standard 62.2-2013. To be acceptable they must not result in excessive energy use. Smart controls will be compared with dehumidifier energy and moisture performance. This work explores the development and performance of smart algorithms for control of mechanical ventilation systems, with the objective of reducing high humidity in modern high performance residences. Simulations of DOE Zero-Energy Ready homes were performed using the REGCAP simulation tool. Control strategies were developed and tested using the Residential Integrated Ventilation (RIVEC) controller, which tracks pollutant exposure in real-time and controls ventilation to provide an equivalent exposure on an annual basis to homes meeting ASHRAE 62.2-2013. RIVEC is used to increase or decrease the real-time ventilation rate to reduce moisture transport into the home or increase moisture removal. This approach was implemented for no-, one- and two-sensor strategies, paired with a variety of control approaches in six humid climates (Miami, Orlando, Houston, Charleston, Memphis and Baltimore). The control options were compared to a baseline system that supplies outdoor air to a central forced air cooling (and heating) system (CFIS) that is often used in hot humid climates. Simulations were performed with CFIS ventilation systems operating on a 33% duty-cycle, consistent with 62.2-2013. The CFIS outside airflow rates were set to 0%, 50% and 100% of 62.2-2013 requirements to explore effects of ventilation rate on indoor high humidity. These simulations were performed with and without a dehumidifier in the model. Ten control algorithms were developed and tested. Analysis of outdoor humidity patterns facilitated smart control development. It was found that outdoor humidity varies most strongly seasonally—by month of the year—and that all locations follow the similar pattern of much higher humidity during summer. Daily and hourly variations in outdoor humidity were found to be progressively smaller than the monthly seasonal variation. Patterns in hourly humidity are driven by diurnal daily patterns, so they were predictable but small, and were unlikely to provide much control benefit. Variation in outdoor humidity between days was larger, but unpredictable, except by much more complex climate models. We determined that no-sensor strategies might be able to take advantage of seasonal patterns in humidity, but that real-time smart controls were required to capture variation between days. Sensor-based approaches are also required to respond dynamically to indoor conditions and variations not considered in our analysis. All smart controls face trade-offs between sensor accuracy, cost, complexity and robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Multiscale dynamics of reactive fronts in the subsurface

1. Brief summary of progress to date. Major Goals of the Project. Overreaching goals of the proposed activity are to develop an or- ganizing framework to describe nonlinear reactive transport in geologic systems over many length and time scales, and corresponding physics-based adaptive multi-scale hybrid models. We will also provide computational tools for robust uncertainty quantification in hybrid simulations. Major Activities of the Project. The major activities in this reporting period have focused on developing a time-averaging framework for ADR transport (Task 1) and global diagnosis criteria for spatio-temporal algorithm refinement (Task 2), as well as numerical validation and verification of the analytical approaches developed. The main activities are as follows: (a) We use homogenization theory to perform a spatio-temporal upscaling of pore-scale advection- diffusion equation in a periodic porous medium in the presence of fluctuating boundary conditions on some parts of the boundary and nonlinear chemical reaction on the solid-liquid interface and we investigate the constraints under which the macroscopic equation gives an accurate explanation of the underlying physics at the pore scale. We demonstrate that the dynamics at the continuum scale is strongly influenced by the interplay between signal frequency at the boundary and transport processes at the pore level. Furthermore, we show that by choosing appropriate scaling factors, a homogenized equation along with expressions for the effective coefficients reliably describe the concentration of solute at the length and time scales vastly different from physically robust micro-scale provided that some specific scale separation constraints are met. Our analysis proves that if the reaction rate or advection at the micro-scale are large, the effectiveness of the homogenized model is impeded. (b) We test numerically the conditions derived theoretically for advective-diffusive and reactive trans- port in a fracture with planar walls subject to temporally oscillating Dirichlet boundary conditions. The numerical analysis confirms the theoretical predictions. Significant Results. Two paper are in preparation. 2. Description of work yet to be accomplished The Tasks yet to be accomplished are listed below: (a) Quantify the impact of uncertainty in wall geometry of planar fractures on predictions hybrids of ARD transport (b) Develop coupling conditions for time hybridzation (c) Quantify the impact of uncertainty in pore geometry on predictions of hybrids of ARD transport in porous media, i.e. develop stochastic hybrid models (d) Use the hybrids combined with X-Ray tomographic images of pore scale structure (from Dr. Gouze, see letter of collaboration) to model dynamic changes in porosity/permeability

42 ENGINEERING↗
DOE OSTI2020

EBSD seed LDRD project: Does Corona Virus – 2019 (COVID-19) and Seasonal Flu have similar meteorology and air quality controls driving their spread?

Seasonal influenza and Influenza like Illnesses (ILI) pose a serious public health risk and in turn affect the economy. Various factors affect ILI cases and mortality, including the pathogen and its interaction with the host, as well as environmental and socioeconomic factors such as meteorology, household structure, air pollution, urbanization, and population. Despite the growing number of studies on influenza and ILI, challenges remain in forecasting the timing of seasonal onset, outbreak patterns, and key factors affecting transmission. In particular, the impacts of meteorology and air quality on ILI have been challenging to understand, with linear regression studies focused on different geographic regions producing contradictory results. For example, influenza seasonality has been associated with cold-dry conditions in temperate mid-latitudes, but with humid-rainy conditions in tropical climates. These apparently contradictory results imply that the relationships between influenza cases and atmospheric variables may be too complex to be captured by linear regression models. In this seed project, we analyze meteorology and air quality variables from numerical models to determine which atmospheric variables are most helpful in predicting weekly changes in recorded flu cases. In contrast to most previous studies that relied on linear regression analysis to predict the timing of the flu onset or peak, we employ a robust machine learning algorithm to evaluate the contribution of atmospheric variables to weekly changes in recorded ILI cases. These results may also be relevant to the spread of other respiratory illnesses such as Corona Virus Disease – 2019 (COVID-19).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Hyperspectral segmentation of plants in fabricated ecosystems

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach provides a scalable solution for spectral segmentation, and facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

Zwart, Petrus H.↗
DOE OSTI2021

New Projections for Dark Matter Searches with Paleo-Detectors

Paleo-detectors are a proposed experimental technique to search for dark matter (DM). In lieu of the conventional approach of operating a tonne-scale real-time detector to search for DM-induced nuclear recoils, paleo-detectors take advantage of small samples of naturally occurring rocks on Earth that have been deep underground (≳5 km), accumulating nuclear damage tracks from recoiling nuclei for O(1)Gyr. Modern microscopy techniques promise the capability to read out nuclear damage tracks with nanometer resolution in macroscopic samples. Thanks to their O(1)Gyr integration times, paleo-detectors could constitute nuclear recoil detectors with keV recoil energy thresholds and 100 kilotonne-yr exposures. This combination would allow paleo-detectors to probe DM-nucleon cross sections orders of magnitude below existing upper limits from conventional direct detection experiments. In this article, we use improved background modeling and a new spectral analysis technique to update the sensitivity forecast for paleo-detectors. We demonstrate the robustness of the sensitivity forecast to the (lack of) ancillary measurements of the age of the samples and the parameters controlling the backgrounds, systematic mismodeling of the spectral shape of the backgrounds, and the radiopurity of the mineral samples. Specifically, we demonstrate that even if the uranium concentration in paleo-detector samples is 10−8 (per weight), many orders of magnitude larger than what we expect in the most radiopure samples obtained from ultra basic rock or marine evaporite deposits, paleo-detectors could still probe DM-nucleon cross sections below current limits. For DM masses ≲ 10 GeV/c2, the sensitivity of paleo-detectors could still reach down all the way to the conventional neutrino floor in a Xe-based direct detection experiment.

Baum, Sebastian (ORCID:0000000167929381)↗
DOE OSTI2016

A COMBINED SPECTROSCOPIC AND PHOTOMETRIC STELLAR ACTIVITY STUDY OF EPSILON ERIDANI

We present simultaneous ground-based radial velocity (RV) measurements and space-based photometric measurements of the young and active K dwarf Epsilon Eridani. These measurements provide a data set for exploring methods of identifying and ultimately distinguishing stellar photospheric velocities from Keplerian motion. We compare three methods we have used in exploring this data set: Dalmatian, an MCMC spot modeling code that fits photometric and RV measurements simultaneously; the FF′ method, which uses photometric measurements to predict the stellar activity signal in simultaneous RV measurements; and H α analysis. We show that our H α measurements are strongly correlated with the Microvariability and Oscillations of STars telescope ( MOST ) photometry, which led to a promising new method based solely on the spectroscopic observations. This new method, which we refer to as the HH′ method, uses H α measurements as input into the FF′ model. While the Dalmatian spot modeling analysis and the FF′ method with MOST space-based photometry are currently more robust, the HH′ method only makes use of one of the thousands of stellar lines in the visible spectrum. By leveraging additional spectral activity indicators, we believe the HH′ method may prove quite useful in disentangling stellar signals.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1984

A research program in active control/aeroelasticity in the JIAFS at NASA Langley Research Center

A control law synthesis methodology for multifunctional active control system to satisfy root-mean-square load and response constraints as well as to meet stability robustness requirements at plant input and output was developed. Modern control theory, singular value analysis and optimization techniques were utilized. All stability and response derivative expressions were derived analytically for sensitivity study. The software is incorporated as an update to the AB/LAD general control design software package PADLOCS.

Whitesides, J. L.↗
NASA NTRS1986

Inherent robustness of discrete-time adaptive control systems

Global stability robustness with respect to unmodeled dynamics, arbitrary bounded internal noise, as well as external disturbance is shown to exist for a class of discrete-time adaptive control systems when the regressor vectors of these systems are persistently exciting. Although fast adaptation is definitely undesirable, so far as attaining the greatest amount of global stability robustness is concerned, slow adaptation is shown to be not necessarily beneficial. The entire analysis in this paper holds for systems with slowly varying return difference matrices; the plants in these systems need not be slowly varying.

Ma, C. C. H.↗