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Structural Pain Compensating Flight Control
The problem of control command and maneuver induced structural loads is an important aspect of any control system design. Designers must design the aircraft structure and the control architecture to achieve desired piloted control responses while limiting the imparted structural loads. The classical approach is to build the structure with high margins, restrict control surface commands to known good combinations, and train pilots to follow procedural maneuvering limitations. With recent advances in structural sensing and the continued desire to improve safety and vehicle fuel efficiency, it is both possible and desirable to develop control architectures that enable lighter vehicle weights while maintaining and improving protection against structural damage.
P-Compensated and P-Doped Superlattice Infrared Detectors
Barrier infrared detectors configured to operate in the long-wave (LW) infrared regime are provided. The barrier infrared detector systems may be configured as pin, pbp, barrier and double heterostructrure infrared detectors incorporating optimized p-doped absorbers capable of taking advantage of high mobility (electron) minority carriers. The absorber may be a p-doped Ga-free InAs/InAsSb material. The p-doping may be accomplished by optimizing the Be doping levels used in the absorber material. The barrier infrared detectors may incorporate individual superlattice layers having narrower periodicity and optimization of Sb composition to achieve cutoff wavelengths of.about.10.mu.m.
Radiation Hardened Structured ASIC Platform with Compensation of Delay for Temperature and Voltage Variations for Multiple Redundant Temporal Voting Latch Technology
The invention relates to devices and methods of maintaining the current starved delay at a constant value across variations in voltage and temperature to increase the speed of operation of the sequential logic in the radiation hardened ASIC design.
Visual end-effector position error compensation for planetary robotics
This paper describes a vision-guided manipulation algorithm that improves arm end-effector positioning to subpixel accuracy and meets the highly restrictive imaging and computational constraints of a planetary robotic flight system. Analytical, simulation-based, and experimental analyses of the algorithm's effectiveness and sensitivity to camera and arm model error is presented along with results on several prototype research systems and ground-in-the-loop technology experiments on the Mars Exploration Rover (MER) vehicles. A computationally efficient and robust subpixel end-effector fiducial detector that is instrumental to the algorithm's ability to achieve high accuracy is also described along with its validation results on MER data.
Azimuth Track Level Compensation to Reduce Blind Pointing Errors of the Deep Space Network Antennas
The Beam wave-guide antennas of the NASA Deep Space Network are -wheel and track- antennas.
Isostatic Compensation of Ishtar Terra, Venus
We have used spherical harmonic representations of the Venus topography and geopotential, obtained from Magellan data, to evaluate isostatic support in several areas within the Ishtar Terra highlands including the Lakshmi plateau, its surrounding mountain belts namely Akna and Freyja, and Maxwell Montes, and the Fortuna Tessera province.
Balanced LQG Compensator for Flexible Structures
The analysis of open-loop balanced flexible structures has been extended for closed-loop structures. The relationships between weights/ variances and characteristic values for the system as well as between weights/variances and plant/estimator pole location are derived in this paper. Thus, the weights can be determined in advance to meet the requirements of a closed-loop system.
Impact of Losing Legacy POES & Aquaand of Gaining Radio Occultation Observations Or Can Increased RO Observations Compensate for the Loss of Legacy POES & Aqua?
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3d Kinematic Passive-steering Control with Roll Compensation for Rover Trajectory Following Over Rough Terrain
Some of the most appealing areas for future planetary surface exploration lie in rough, uneven terrains, such as craters and cold traps, which are currently inaccessible by state-of-the-art robotic systems [1]. To provide modularity for access and in-situ sampling within such extreme environments, Jet Propulsion Laboratory (JPL) and California Institute of Technology have collaborated to develop the DuAxel rover system, a modular robot composed of two Axel rappelling vehicles docked to a central module into a four-wheeled configuration, suited for driving long distances due to articulated passivesteering capabilities [2]. Inspired by Carnegie Mellon’s Zoë rover [3], a 3D kinematic control strategy, leveraging a novel, DuAxel-centric model, has been developed to enable precise trajectory following over rough, flat terrain with presence of obstacles.
3d Kinematic Passive-steering Control with Roll Compensation for Rover Trajectory Following Over Rough Terrain
No abstract provided
Process Compensated Resonance Testing (PCRT)
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A Machine Learning Framework for Error Compensation in Radiative Transfer Calculations
Radiative heat transfer influences the amount of heat flux transferred to the surface of the hypersonic vehicle, which is essential to evaluate the performance of thermal protection systems. The radiative heat flux is found to be computationally prohibitive while accounting for the variation in spatial, angular, and spectral domains. A new methodology has been recently developed to alleviate the cost of computation in the spectral domain by constructing flow-agnostic reduced-order models (ROMs). The developed spectral ROM databases provide grouping strategies that account for non-equilibrium absorption and emission as well as interaction between disparate species due to spectral overlap in associated radiative processes. However, the developed ROMs need to be optimized for a specific combination of interacting gas species and would need to re-calibrated in case individual species are added/omitted. In this work, we use various machine learning (ML) techniques to approximate the radiative intensities determined by a ROM optimized for a specific gas mixture. The ML model relies on the ROM databases developed for a single species which ignores any spectral overlap. Thus, radiation evaluation starts with a simple summation of radiative intensities predicted using these non-calibrated ROMs for the contributing species. The ML framework then provides a correction to account for the interplay in the frequency, i.e., emission of photons by one species and absorption by another, and yields mixture-specific radiation fields. Once trained on the individual ROM databases, the ML framework offers instantaneous corrections that serves as a time/cost effective alternative to the optimization of ROMs for a specific gas mixture. The ML framework is trained on both the high fidelity and ROM evaluated line of sight (LOS) data from Orion, Stardust, and FIRE II cases to obtain a general purpose correction model for earth re-entry scenarios when radiation contributions from both atomic nitrogen and atomic oxygen are considered. A geometric length scale parameter is used in the training process to account for errors introduced in the ROM databases as a consequence of high optical thickness. The efficacy of the ML framework is underscored through extensive analysis of train and test errors with respect to all the re-entry scenarios. The applicability of such an ML framework was further corroborated by embedding it in a state-of-the-art US3D - NERO system for determining the radiative heat flux transferred to the hypersonic vehicle surface.
Compensating for gyroradius effects in beamlines with small Helmholtz coils
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