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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

FIP BIAS EVOLUTION IN A DECAYING ACTIVE REGION

Solar coronal plasma composition is typically characterized by first ionization potential (FIP) bias. Using spectra obtained by Hinode’s EUV Imaging Spectrometer instrument, we present a series of large-scale, spatially resolved composition maps of active region (AR)11389. The composition maps show how FIP bias evolves within the decaying AR during the period 2012 January 4–6. Globally, FIP bias decreases throughout the AR. We analyzed areas of significant plasma composition changes within the decaying AR and found that small-scale evolution in the photospheric magnetic field is closely linked to the FIP bias evolution observed in the corona. During the AR’s decay phase, small bipoles emerging within supergranular cells reconnect with the pre-existing AR field, creating a pathway along which photospheric and coronal plasmas can mix. The mixing timescales are shorter than those of plasma enrichment processes. Eruptive activity also results in shifting the FIP bias closer to photospheric in the affected areas. Finally, the FIP bias still remains dominantly coronal only in a part of the AR’s high-flux density core. We conclude that in the decay phase of an AR’s lifetime, the FIP bias is becoming increasingly modulated by episodes of small-scale flux emergence, i.e., decreasing the AR’s overall FIP bias. Our results show that magnetic field evolution plays an important role in compositional changes during AR development, revealing a more complex relationship than expected from previous well-known Skylab results showing that FIP bias increases almost linearly with age in young ARs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Some assembly required: assembly bias in massive dark matter halos

Here, we study halo assembly bias for cluster-sized halos. Previous work has found little evidence for correlations between large-scale bias and halo mass assembly history for simulated cluster-sized halos, in contrast to the significant correlation found between bias and concentration for halos of this mass. This difference in behavior is surprising, given that both concentration and assembly history are closely related to the same properties of the linear-density peaks that collapse to form halos. Using publicly available simulations, we show that significant assembly bias is indeed found in the most massive halos with M ~ 10 15 M ⊙ , using essentially any definition of halo age. For lower halo masses M ~ 10 14 M ⊙ , no correlation is found between bias and the commonly used age indicator a 0.5 , the half-mass time. We show that this is a mere accident, and that significant assembly bias exists for other definitions of halo age, including those based on the time when the halo progenitor acquires some fraction f of the ultimate mass at z =0 . For halos with M vir ~ 10 14 M ⊙ , the sense of assembly bias changes sign at f =0.5 . We explore the origin of this behavior, and argue that it arises because standard definitions of halo mass in halo finders do not correspond to the collapsed, virialized mass that appears in the spherical collapse model used to predict large-scale clustering. Because bias depends strongly on halo mass, these errors in mass definition can masquerade as or even obscure the assembly bias that is physically present. Lastly, more physically motivated halo definitions using splashback should be free of this particular defect of standard halo finders.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.

Chowdhury, Shovan↗
DOE OSTI2016

Moist Process Biases in Simulations of the Madden–Julian Oscillation Episodes Observed during the AMIE/DYNAMO Field Campaign

Two Madden–Julian oscillation (MJO) episodes observed during the 2011 Atmospheric Radiation Measurement Program MJO Investigation Experiment (AMIE)/DYNAMO field campaign are simulated using a regional model with various cumulus parameterizations, a regional cloud-permitting model, and a global variable-resolution model with a high-resolution region centered over the tropical Indian Ocean. Model biases in relationships relevant to existing instability theories of MJO are examined and their relative contributions to the overall model errors are quantified using a linear statistical model. The model simulations capture the observed approximately log-linear relationship between moisture saturation fraction and precipitation, but precipitation associated with the given saturation fraction is overestimated especially at low saturation fraction values. This bias is a major contributor to the excessive precipitation during the suppressed phase of MJO. After accounting for this bias using a linear statistical model, the spatial and temporal structures of the model-simulated MJO episodes are much improved, and what remains of the biases is strongly correlated with biases in saturation fraction. The excess precipitation bias during the suppressed phase of the MJO episodes is accompanied by excessive column-integrated radiative forcing and surface evaporation. A large portion of the bias in evaporation is related to biases in wind speed, which are correlated with those of precipitation. Furthermore, these findings suggest that the precipitation bias sustains itself at least partly by cloud radiative feedbacks and convection–surface wind interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Humidity Bias and Effect on Simulated Aerosol Optical Properties during the Ganges Valley Experiment

The radiosonde humidity profiles available during the Ganges Valley Experiment were compared to those simulated from the regional Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF -Chern) and the global reanalysis datasets. Large biases were revealed. On a monthly mean basis at Nainital, located in northern India, the WRFChern model simulates a large moist bias in the free troposphere (up to +20%) as well as a large dry bias in the boundary layer (up to -30%). While the overall pattern of the biases is similar, the magnitude of the biases varies from time to time and from one location to another. At Thiruvananthapuram, the magnitude of the dry bias is smaller, and in contrast to Nainital, the higher-resolution regional WRF -Chern model generates larger moist biases in the upper troposphere than the global reanalysis data. Furthermore, the humidity biases in the upper troposphere, while significant, have little impact on the model estimation of column aerosol optical depth (AOD). The frequent occurrences of the dry boundary-layer bias simulated by the large-scale models tend to lead to the underestimation of AOD. It is thus important to quantify the humidity vertical profiles for aerosol simulations over South Asia.

aerosol extinction↗
NASA NTRS2012

Cognitive Bias in the Verification and Validation of Space Flight Systems

Cognitive bias is generally recognized as playing a significant role in virtually all domains of human decision making. Insight into this role is informally built into many of the system engineering practices employed in the aerospace industry. The review process, for example, typically has features that help to counteract the effect of bias. This paper presents a discussion of how commonly recognized biases may affect the verification and validation process. Verifying and validating a system is arguably more challenging than development, both technically and cognitively. Whereas there may be a relatively limited number of options available for the design of a particular aspect of a system, there is a virtually unlimited number of potential verification scenarios that may be explored. The probability of any particular scenario occurring in operations is typically very difficult to estimate, which increases reliance on judgment that may be affected by bias. Implementing a verification activity often presents technical challenges that, if they can be overcome at all, often result in a departure from actual flight conditions (e.g., 1-g testing, simulation, time compression, artificial fault injection) that may raise additional questions about the meaningfulness of the results, and create opportunities for the introduction of additional biases. In addition to mitigating the biases it can introduce directly, the verification and validation process must also overcome the cumulative effect of biases introduced during all previous stages of development. A variety of cognitive biases will be described, with research results for illustration. A handful of case studies will be presented that show how cognitive bias may have affected the verification and validation process on recent JPL flight projects, identify areas of strength and weakness, and identify potential changes or additions to commonly used techniques that could provide a more robust verification and validation of future systems.

Larson, Steve↗
NASA NTRSDate not supplied

Cold-Point Tropopause Temperature Bias in Reanalyses Modulated By Equatorial Waves

In this reanalysis inter-comparison study, we compare the modeled Cold-point tropopause temperature (CPT from model level data) near the equator to that of multi-mission GNSS-RO within a preliminary time period of 2007-2011. The known reanalysis CPT temperature warm bias is studied relative to the phase of equatorial waves filtered from GNSS-RO at tropopause level. This uncovers a common denominator in the CPT temperature bias behavior in all reanalyses: - When equatorial waves are present, within their cold phase the reanalysis bias is increased by over 1K, i.e. >1K on top of the reanalysis average warm bias. - Within an equatorial wave’s warm phase, the opposite is observed, with the reanalysis CPT temperature bias even being reversed at higher equatorial wave amplitudes, i.e. the modeled CPT becoming too cold in these cases. - With no equatorial wave anomalies present, the reanalysis bias remains at the average reported in previous inter-comparisons that looked at zonal-mean CPT biases. Inertia-Gravity and Mixed Rossby-Gravity waves show this behavior most clearly, Kelvin waves show a similar modulation but with half of the magnitude, and equatorial Rossby wave modulation of CPT reanalysis bias is still apparent but with further decreased magnitude. This hints on a time-space scale-dependence, with medium-smaller-scale and faster equatorial waves influencing CPT reanalysis bias the most. For comparison, the large-scale and slow-moving MJO does not show any of the effects described above. Current work focuses on the data assimilation response of the reanalysis systems, again relative to the equatorial wave’s phases.

Tropopause↗
DOE OSTI2023

Summertime Near-Surface Temperature Biases Over the Central United States in Convection-Permitting Simulations

Convection-Permitting Model (CPM) simulations of the Central United States climate for the summer of 2011 are studied to understand the causes of warm biases in 2-m air temperature (T 2m ) and related underestimates of precipitation including that from mesoscale convective systems (MCSs). Based on 10 CPM simulations and 9 coarser-resolution model simulations, we quantify contributions from evaporative fraction (EF) and radiation to the T 2m bias with both types of models overestimating T 2m largely because they underestimate EF. The performance of CPMs in capturing MCS characteristics (frequency, rainfall, propagation) varies. The pre-summer precipitation bias has large correlation with mean summertime T 2m bias but the relationship between summertime MCS mean rainfall bias and T 2m bias is non-monotonic. Analysis of lifting condensation level deficit and convective available potential energy suggests that models with T 2m warm biases and low EF have too dry and stable boundary layers, inhibiting the formation of clouds, precipitation and MCSs. Among the CPMs with differing model formulations (e.g., transpiration, infiltration, cloud macrophysics and microphysics), evidence suggests that altering the land-surface model is more effective than altering the atmospheric model in reducing T 2m biases. In conclusion, these results demonstrate that land-atmosphere interactions play a very important role in determining the summertime climate of the Central United States.

2-m air temperature↗
DOE OSTI2022

Toroidal modeling of 3D perturbations generated by current filaments in scrape-off layer in tokamak with biased divertor targets

The divertor biasing technique offers a promising alternative to control the edge localized mode (ELM) as well as the divertor heat load in tokamaks, as compared to the resonant magnetic perturbation (RMP) generated by magnetic coils. The linear resistive MHD code MARS-F (Liu et al 2000 Phys. Plasmas 7 3681) is employed to study the plasma response to the n = 2 (n is the toroidal mode number) field perturbation in HL-2A, produced by the divertor biasing current filaments flowing in the scrape-off layer region. The response field due to biasing currents is compared with the RMP field utilized for ELM control in HL-2A. The strength of the radial resonant field perturbation, produced by the biasing currents of 100 A level, is found to be comparable with RMP generated by several kA of ELM control coil currents for the reference plasma. The plasma normal displacement near the X-point and the associated neoclassical toroidal viscosity torque are also computed to be similar between these two techniques. The modeling results thus strongly suggest that the biasing technique can be applied to control ELMs. Moreover, the biasing currents produce field perturbations, including the plasma response, that are localized more near the plasma edge (compared to the RMP counterpart), thus reducing the chance of mode locking associated with core perturbations. Particle orbit tracing also reveals that the biasing current produced magnetic perturbation tends to widen the heat deposition region and induce the strike point splitting of the ion saturation flow on the outer divertor surface, consistent with experimental observations in HL-2A. These toroidal modeling results confirm the possibility of ELM control and plasma exhaust solution by the divertor biasing technique.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Diagnosing Undersampling Biases in Monte Carlo Eigenvalue and Flux Tally Estimates

Here, this study focuses on understanding the phenomena in Monte Carlo simulations known as undersampling, in which Monte Carlo tally estimates may not encounter a sufficient number of particles during each generation to obtain unbiased tally estimates. Steady-state Monte Carlo simulations were performed using the KENO Monte Carlo tools within the SCALE code system for models of several burnup credit applications with varying degrees of spatial and isotopic complexities, and the incidence and impact of undersampling on eigenvalue and flux estimates were examined. Using an inadequate number of particle histories in each generation was found to produce a maximum bias of ~100 pcm in eigenvalue estimates and biases that exceeded 10% in fuel pin flux tally estimates. Having quantified the potential magnitude of undersampling biases in eigenvalue and flux tally estimates in these systems, this study then investigated whether Markov Chain Monte Carlo convergence metrics could be integrated into Monte Carlo simulations to predict the onset and magnitude of undersampling biases. Five potential metrics for identifying undersampling biases were implemented in the SCALE code system and evaluated for their ability to predict undersampling biases by comparing the test metric scores with the observed undersampling biases. Finally, of the five convergence metrics that were investigated, three (the Heidelberger-Welch relative half-width, the Gelman-Rubin $\hat{R}_c$ diagnostic, and tally entropy) showed the potential to accurately predict the behavior of undersampling biases in the responses examined.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

An Analysis of Gravity-Field Estimation Based on Intersatellite Dual-1-Way Biased Ranging

The GRACE (Gravity Recovery And Climate Experiment) mission is designed to make global, highly accurate measurements of the Earth's gravity field with high spatial resolution. Ancillary GPS occultation measurements are also to be carried out for atmospheric monitoring. In the dual-1-way biased ranging of this mission, the range between two satellites separated by 100 to 200 km in nearly polar, coplanar, circular orbits, is measured to very high precision, to within an additive constant, through the exchange of K- and Ka-band sinusoidal signals. Such biased ranging data, along with GPS L-band range and phase data, can be processed and fit over successive multiday intervals to obtain accurate estimates of the Earth's gravity field. This report approximately models and analyzes this process, from the generation of the RF signals at the two satellites through the extraction of the geopotential. The steps include generation of the transmitted signals, processing the received signals to extract high-rate baseband phase, carrying out a dual-1-way combination of baseband phase to extract high-rate biased range for each band, combining K- and Ka-band ranges to correct for the ionosphere effect, and processing the resulting high-rate biased range values to extract three types of reduced-rate observables: biased range, range rate and range acceleration. The version of dual-1-way biased ranging developed by this report improves upon previous versions in a number of ways: highly accurate satellite-timetag corrections derived from concurrent GPS data, better baseband phase extraction using highly digital processing, highly accurate USO-rate calibration derived from concurrent GPS data, an improved method for extracting high-rate biased range from baseband phase, improved filtering for extracting reduced- rate observables from high-rate biased range, and parallel extraction of three observable types.

SINUSOIDAL SIGNALS↗
DOE OSTI2018

Bridging the simulator gap: Measuring motivational bias in digital nuclear power plant environments

Using digital NPP simulator facilities for research in Human Reliability Analysis (HRA) and nuclear power research is increasingly popular. For this work, we propose a method for characterizing and quantifying the gap between data collected in a simulator and data that reflect NPP operations. Using novice operators, we demonstrate how to manipulate and measure the impact of the simulator environment on operator actions. A set of biases are proposed to characterize factors introduced by the simulator environment. There are two categories of simulator bias: Environmental Biases (physical differences between the simulator and the control room), and Motivational Biases (cognitive differences between training in a simulator and operating a NPP). This study examines Motivational Bias. A preliminary causal model of Motivational Biases is introduced and tested in a demonstration experiment using 30 student operators. Data from 41 simulator sessions are analyzed. Data include crew characteristics, operator surveys, and time to recognize and diagnose the accident in the scenario. Quantitative models of the Motivational Biases using Structural Equation Modeling (SEM) are proposed. With these models, we estimate how the effects of the scenario conditions are mediated by simulator bias, and we demonstrate how to quantify the strength of these effects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States

Past studies have shown that climate simulations have substantial warm and dry biases during the summer in the conterminous United States (CONUS), particularly in the central Great Plains (CGP). These biases have critical implications for the interpretation of climate change projections, but the complex overlap of multiple land-atmosphere feedback processes make them difficult to explain (and therefore correct). Even though surface soil moisture (SM) is often cited as a key control variable in these processes, there are still knowledge gaps about its specific role. Here, we use recently developed remotely sensed SM products to analyse the link between spatial patterns of summertime SM, precipitation and air temperature biases over CONUS in 20 different CMIP5 simulations. We identify three main types of bias combinations: (i) a dry/warm bias over the CGP region, with a significant inter-model correlation between SM and air temperature biases (R = -0.65), (ii) a wet/cold bias in NW CONUS, and (iii) a dry/cold bias in SW CONUS. Combined with irrigation patterns, these results suggest that land-atmosphere feedbacks over the CGP are not only local but have a regional dimension, and demonstrate the added-value of large-scale SM observations for resolving the full feed-back loop between precipitation and temperature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Two-fluid biasing simulations of the large plasma device

External biasing of the Large Plasma Device (LAPD) and its impact on plasma flows and turbulence are explored for the first time in 3D simulations using the Global Braginskii Solver code. Without external biasing, the LAPD plasma spontaneously rotates in the ion diamagnetic direction. The application of a positive bias increases the plasma rotation in the simulations, which show the emergence of a coherent Kelvin Helmholtz (KH) mode outside of the cathode edge with poloidal mode number m≃6. Negative biasing reduces the rotation in the simulations, which exhibit KH turbulence modestly weaker than but otherwise similar to unbiased simulations. Biasing either way, but especially positively, forces the plasma potential inside the cathode edge to a spatially constant, KH-stable profile, leading to a more quiescent core plasma than the unbiased case. A moderate increase in plasma confinement and an associated steepening of the profiles are seen in the biasing runs. The simulations thus show that the application of external biasing can improve confinement while also driving a Kelvin-Helmholtz instability. Ion-neutral collisions have only a weak effect in the biased or unbiased simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Optical selection bias and projection effects in stacked galaxy cluster weak lensing

ABSTRACT Cosmological constraints from current and upcoming galaxy cluster surveys are limited by the accuracy of cluster mass calibration. In particular, optically identified galaxy clusters are prone to selection effects that can bias the weak lensing mass calibration. We investigate the selection bias of the stacked cluster lensing signal associated with optically selected clusters, using clusters identified by the redMaPPer algorithm in the Buzzard simulations as a case study. We find that at a given cluster halo mass, the residuals of redMaPPer richness and weak lensing signal are positively correlated. As a result, for a given richness selection, the stacked lensing signal is biased high compared with what we would expect from the underlying halo mass probability distribution. The cluster lensing selection bias can thus lead to overestimated mean cluster mass and biased cosmology results. We show that the lensing selection bias exhibits a strong scale dependence and is approximately 20–60 per cent for ΔΣ at large scales. This selection bias largely originates from spurious member galaxies within ±20–60 $h^{-1}\, \rm Mpc$ along the line of sight, highlighting the importance of quantifying projection effects associated with the broad redshift distribution of member galaxies in photometric cluster surveys. While our results qualitatively agree with those in the literature, accurate quantitative modelling of the selection bias is needed to achieve the goals of cluster lensing cosmology and will require synthetic catalogues covering a wide range of galaxy–halo connection models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Systematic Biases in Weak Lensing Cosmology with the Dark Energy Survey

This thesis sets out a practical guide to applying shear measurements as a cosmological tool. We first present one of two science-ready galaxy shape catalogues from Year 1 of the Dark Energy Survey (DES Y1), which covers 1500 square degrees in four bands $griz$, with a median redshift of $0.59$. We describe the shape measurement process implemented by the DES Y1 imshape catalogue, which contains 21.9 million high-quality $r$-band bulge/disc fits. In Chapter 3 a new suite of image simulations, referred to as Hoopoe, are presented. The Hoopoe dataset is tailored to DES Y1 and includes realistic blending, spatial masks and variation in the point spread function. We derive shear corrections, which we show are robust to changes in calibration method, galaxy binning and variance within the simulated dataset. Sources of systematic uncertainty in the simulation-based shear calibration are discussed, leading to a final estimate of the $1\sigma$ uncertainties in the residual multiplica tive bias after calibration of 0.025. Chapter 4 describes an extension of the analysis on the Hoopoe simulations into a detailed investigation of the impact of galaxy neighbours on shape measurement and shear cosmology. Four mechanisms by which neighbours can have a non-negligible influence on shear measurement are identified. These effects, if ignored, would contribute a net multiplicative bias of $m \sim 0.03 - 0.09$ in DES Y1, though the precise impact will depend on both the measurement code and the selection cuts applied. We use the cosmological inference pipeline of DES Y1 to explore the cosmological implications of neighbour bias and show that omitting blending from the calibration simulation for DES Y1 would bias the inferred clustering amplitude $S_8 \equiv \sigma_8 (\omegam /0.3)^{0.5}$ by $1.5 \sigma$ towards low values. Finally, we use the Hoopoe simulations to test the effect of neighbour-induced spatial correlations in the multiplicative bias. We find the cosmo logical impact to be subdominant to statistical error at the! current level of precision. Another major uncertainity in shear cosmology is the accuracy of our ensemble redshift distributions. Chapter 5 presents a numerical investigation into the combined constraining power of cosmic shear, galaxy clustering and their cross-correlation in DES Y1, and the potential for internal calibration of redshift errors. Introducing a moderate uniform bias into the redshift distributions used to model the weak lensing (WL) galaxies is shown to produce a $> 2\sigma$ bias in $S_8$. We demonstrate that this cosmological bias can be eliminated by marginalising over redshift error nuisance parameters. Strikingly, the cosmological constraint of the combined dataset is largely undiminished by the loss of prior information on the WL distributions. We demonstrate that this implicit self-calibration is the result of complementary degeneracy directions in the combined data. In Chapter 6 we present the preliminary results of an investigation into galaxy intrin sic alignments. Using the DES Y1 data, we show a clear dependence in alignment amplitude on galaxy type, in agreement with previous results. We subject these findings to a series of initial robustness tests. We conclude with a short overview of the work presented, and discuss prospects for the future.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1990

Bias and design in software specifications

Implementation bias in a specification is an arbitrary constraint in the solution space. Presented here is a model of bias in software specifications. Bias is defined in terms of the specification process and a classification of the attributes of the software product. Our definition of bias provides insight into both the origin and the consequences of bias. It also shows that bias is relative and essentially unavoidable. Finally, we describe current work on defining a measure of bias, formalizing our model, and relating bias to software defects.

Straub, Pablo A.↗
NASA NTRS2012

Adaptive Control in the Presence of Simultaneous Sensor Bias and Actuator Failures

The problem of simultaneously accommodating unknown sensor biases and unknown actuator failures in uncertain systems is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor biases and actuator faults may be present at the outset or may occur at unknown instants of time during operation. A modified MRAC law is proposed, which combines sensor bias estimation with control gain adaptation for accommodation of sensor biases and actuator failures. This control law is shown to provide signal boundedness in the resulting system. For the case when an external asymptotically stable sensor bias estimator is available, an MRAC law is developed to accomplish asymptotic state tracking and signal boundedness. For a special case wherein biases are only present in the rate measurements and bias-free position measurements are available, an MRAC law is developed using a model-independent bias estimator, and is shown to provide asymptotic state tracking with signal boundedness.

Joshi, Suresh M.↗