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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 OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addressed the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models---the so-called outer loop. This report summarizes the work done under DE-SC0021077 on (1) nonlocal models for solidification problems, (2) a multifidelity method for a nonlocal diffusion model, and (3) multifidelity Monte Carlo methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems (Final Report for AEOLUS)

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for: (1) learning predictive models from data; and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addresses the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models. This report summarizes the key highlights of our research during the period of performance.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Vehicle Steering control: A model of learning

A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Car-driver describing functions were calculated, the probable control structure determined, and the driver-alone transfer function modelled. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

Smiley, A.↗
NASA NTRS1978

A model for sensorimotor control and learning

A model for motor learning, generalization, and adaptation is presented. It is shown that the equations of motion of a limb can be expressed in a parametric form that facilitates transformation of desired trajectories into plans. These parametric equations are used in conjunction with a quantized multi-dimensional memory organized by state variables. The memory is supplied with data derived from the analysis of practice movements. A small computer and mechanical arm are used to implement the model and study its properties. Results verify the ability to acquire new movements, adapt to mechanical loads, and generalize between similar movements.

Raibert, M. H.↗
NASA NTRS1982

The relationship of motion sickness susceptibility to learned autonomic control for symptom suppression

Twenty-four men were randomly assigned to four equal groups matched in terms of their Coriolis Sickness Susceptibility Index (CSSI). Two groups of subjects were highly susceptible to motion sickness, and two groups were moderately susceptible. All subjects were given six C551 tests at 5-d intervals. Treatment Groups I (highly susceptible) and II (moderately susceptible) were taught to control their autonomic responses, using a training method called autogenic-feedback training (AFT) before the third, fourth, and fifth CSSI tests. Control groups III (highly susceptible) and IV (moderately susceptible) received no treatment. Results showed that both treatment groups significantly improved performance on CSSI tests after training; neither of the control groups changed significantly. Highly and moderately susceptible subjects in the two treatment groups improved at comparable rates. Highly susceptible control group subjects did not habituate across tests as readily as the moderately susceptible controls.

Cowings, P. S.↗
NASA NTRS1991

Lessons learned in control center technologies and non-technologies

Information is given in viewgraph form on the Solar Mesosphere Explorer (SME) Control Center and the Oculometer and Automated Space Interface System (OASIS). Topics covered include SME mission operations functions; technical and non-technical features of the SME control center; general tasks and objects within the Space Station Freedom (SSF) ground system nodes; OASIS-Real Time for the control and monitoring of of space systems and subsystems; and OASIS planning, scheduling, and PC architecture.

Hansen, Elaine R.↗
NASA NTRS1993

Learning Search Control Knowledge for Deep Space Network Scheduling

While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.

Deep Space Network Search Control↗
DOE OSTI2024

Machine learning informed control systems for extrusion printing processes

Systems and methods for controlling a material extrusion device to extrude a filament of an ink are provided. An extrusion printing control system collects from one or more sensors measurements representing an internal state of material extrusion processing during extrusion of the filament. In addition, the system collects an image of the filament as the filament is extruded. The system applies a classifier to the collected image to generate an image-derived state characterizing the filament. Based on the internal state and the image-derived state, the system estimates a derived state using a model. The system determines control parameters using the model to achieve a desired quality of the filament by minimizing a cost function based on the internal state, the image-derived state, the derived state, and constraints of the material extrusion device. Finally, the system provides the control parameters to a controller of the material extrusion device.

Howell, Brian↗