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Results for “Predictive control”

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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Orbit determination support for Hiten's aerobraking in the Earth's atmosphere

Two passes of the ISAS (Japan's Institute of Space and Astronautical Science) Hiten spacecraft through the Earth's atmosphere, at perigee altitudes of 125 km and 120 km, during Mar. 1991 marked the first aerobraking technology demonstrations for an object in cis-lunar orbit traveling at near Earth escape velocity. Prediction and control of perigee altitude to better than 1 km was desired to assure spacecraft survival. Covariance analysis provided confidence that prediction accuracy better than 200 m for support of final trim maneuver design was achievable with NASA DSN (Deep Space Network) tracking support. ISAS used orbit determination results in their decisions to cancel final trim maneuvers. Post flight reconstructions, marking the first combined use of DSN and ISAS tracking data, yielded perigee altitude solutions which agree with the near real time mission operations support predictions to better than 50 m.

Efron, L.↗
DOE OSTI2024

Towards accurate prediction of configurational disorder properties in materials using graph neural networks

Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

Chemistry↗
NASA NTRS1977

Prediction of jump phenomena in rotationally-coupled maneuvers of aircraft, including nonlinear aerodynamic effects

An analytical method has been developed for predicting critical control inputs for which nonlinear rotational coupling may cause sudden jumps in aircraft response. The analysis includes the effect of aerodynamics which are nonlinear in angle of attack. The method involves the simultaneous solution of two polynomials in roll rate, whose coefficients are functions of angle of attack and the control inputs. Results obtained using this procedure are compared with calculated time histories to verify the validity of the method for predicting jump-like instabilities.

Young, J. W.↗
NASA NTRS2001

2001 Flight Mechanics Symposium

This conference publication includes papers and abstracts presented at the Flight Mechanics Symposium held on June 19-21, 2001. Sponsored by the Guidance, Navigation and Control Center of Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to attitude/orbit determination, prediction and control; attitude simulation; attitude sensor calibration; theoretical foundation of attitude computation; dynamics model improvements; autonomous navigation; constellation design and formation flying; estimation theory and computational techniques; Earth environment mission analysis and design; and, spacecraft re-entry mission design and operations. Government, industry, and the academic community participated in the preparation and presentation of these papers.

Flight mechanics↗
DOE OSTI2020

Gravitational Test beyond the First Post-Newtonian Order with the Shadow of the M87 Black Hole

In this study, the 2017 Event Horizon Telescope (EHT) observations of the central source in M87 have led to the first measurement of the size of a black-hole shadow. This observation offers a new and clean gravitational test of the black-hole metric in the strong-field regime. We show analytically that spacetimes that deviate from the Kerr metric but satisfy weak-field tests can lead to large deviations in the predicted black-hole shadows that are inconsistent with even the current EHT measurements. We use numerical calculations of regular, parametric, non-Kerr metrics to identify the common characteristic among these different parametrizations that control the predicted shadow size. We show that the shadow-size measurements place significant constraints on deviation parameters that control the second post-Newtonian and higher orders of each metric and are, therefore, inaccessible to weak-field tests. The new constraints are complementary to those imposed by observations of gravitational waves from stellar-mass sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Machine learning to predict final fire size at the time of ignition

Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4 ± 5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Nanoparticle Catalyst Formation and Subsequent Sintering: Size-Distribution Fundamental Understanding and Catalyst Control via Mechanism-Enabled Population Balance Modeling (Final Report)

The challenge of elucidating the chemical mechanisms by which nano- and other particles form, and then using those mechanisms to understand, analyze, and ideally predict particle-size and particle-size distributions, was undertaken. Thanks to multi-year, longer-term DOE support each of these challenges was successfully met. The community is now able to both predict, and control in cases where the underlying mechanism of particle formation has been determined, the key PS and PSD that are, in turn, critical properties that strongly influence the catalytic and other physical properties of nano- and larger particles across nature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Charge Management for an Inductively Charged On-Demand Battery-Electric Shuttle Service with High Penetration of Renewable Energy

This paper presents a charge management control strategy for an on-demand battery-electric shuttle van operating at the National Renewable Energy Laboratory (NREL) campus and supported by day-time inductive charging at the vehicle's waiting spot. A new control algorithm has been proposed for reducing the demand charge costs incurred from wireless charging of the on-demand shuttle. A custom controller has been developed to monitor the shuttle, wireless charger, renewable energy generation, and various loads at NREL's campus, and regulate charging behavior for demand response. The intermittent renewable generation and sporadic operation of the on-demand shuttle service contribute to a high level of uncertainty in expected campus load profile, which must be carefully managed. The control algorithm predicts energy profile to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller has been designed and optimized using a Simulink model for the entire system. Next, it has been implemented and tested in real-time on the NREL campus. Two primary vehicle-use cases, charge sustaining and charge depletion operation, are tested under different load profiles and drive cycles to assess the controller's effectiveness at reducing peak demand and therefore demand charges. The proposed controller showed robust performance under different driving scenarios with high correlation between simulated and experimental data. The results showed that proper demand response can be achieved with an average of 94% reduction of charging loads during peak demand events.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
NASA NTRSDate not supplied

Automated Airspace Management: Concept, Development, and Testing

For many decades, researchers at NASA Ames Research Center have worked to make the air-transportation system more efficient, predictable, and effective. Since about 2005 one important aspect of this research has been the development of an autonomous system for air-traffic control. This system, known as the Autoresolver, is designed to perform most of the roles that air-traffic controllers perform including ensuring separation between aircraft, creating routes around weather and other avoidance volumes, and sequencing and scheduling aircraft across points in space. The recent, rapid expansion of new aircraft operations and types, including urban air mobility aircraft and small unmanned aerial systems, have only increased the need for highly automated systems to control the predicted traffic demand. This talk will focus on the development of the Autoresolver - from concept to testing. It will also discuss the National Airspace (NAS) Digital Twin simulation platform, created to facilitate rapid testing and improvement of the algorithm and with the hope of proving the automation in a high-fidelity environment. An open question that will be discussed is how to ensure that the system-level emergent behavior of independently developed autonomous algorithms is what is desired.

autonomy↗
NASA NTRS1988

PACOSS program

The objectives of the PACOSS program were to demonstrate the respective roles of passive and active control for structures that represented future Large Space Structures (LSS), to develop means to introduce passive vibration control, and to experimentally verify the damping predictions and the control algorithms. In order to meet the objectives, the program was divided into an analytical simulation phase to establish the respective roles of passive and active damping on a LSS-type structure, and a design, analysis, and test phase to validate the passive damping and the control algorithm performance for a structure. Predictable amounts of damping can be designed into a LSS structure, the best control strategy uses a combination of passive damping and active controls, and a more optimum system can be achieved by an early interaction between the structural designer, controls engineer, and the damping designer.

Richards, K. E., Jr.↗
DOE OSTI2020

Optimization-based control using input convex neural networks

Input convex neural networks (ICNNs) are a family of deep learning models where the outputs are constructed to be convex functions of the inputs. By parameterizing system models using ICNNs, optimization-based control problems can be solved as convex optimization problems, leading to improved performance and robustness. This work proposes a novel framework where the control objective function and constraints are modelled using ICNNs. A case study of optimization-based control with output constraints is conducted on a process with input multiplicity and nonminimum phase behavior. The simulation results demonstrate improved economic yield compared with normal neural networks. Additionally, the input convexity formulation is compared with simple regularization techniques, and unique benefits such as improved data efficiency and robustness of the proposed formulation are shown. Finally, by explicitly incorporating prior knowledge about convexity, this framework provides a good balance between the universal approximation power of deep learning and computational feasibility required by control.

42 ENGINEERING↗
NASA NTRS1973

IMP-J attitude control prelaunch analysis and operations plan

A description of the attitude control support being supplied for the Explorer 50 mission is given. Included in the document are descriptions of the computer programs being used to support attitude determination, prediction, and control for the mission and descriptions of the operating procedures that will be used to accomplish mission objectives.

Hooper, H. L.↗
DOE OSTI2023

Assessing the Impact of Cybersecurity Attacks on Energy Systems

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks.

buildings↗
NASA NTRS1990

Prediction of force and acceleration control spectra for Space Shuttle orbiter sidewall-mounted payloads

The development of force and acceleration control spectra for vibration testing of Space Shuttle (STS) orbiter sidewall-mounted payloads requiresreliable estimates of the sidewall apparent weight and free (i.e. unloaded) vibration during lift-off. The feasibility of analytically predicting these quantities has been investigated through the development and analysis of a finite element model of the STS cargo bay. Analytical predictions of the sidewall apparent weight were compared with apparent weight measurements made on OV-101, and analytical predictions of the sidewall free vibration response during lift-off were compared with flight measurements obtained from STS-3 and STS-4. These analysis suggest that the cargo bay finite element model has potential application for the estimation of force and acceleration control spectra for STS sidewall-mounted payloads.

Hipol, Philip J.↗
DOE OSTI2021

AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

Wang, Z↗
NASA NTRS1973

AE-C attitude determination and control prelaunch analysis and operations plan

A description of attitude control support being supplied by the Mission and Data Operations Directorate is presented. Included are descriptions of the computer programs being used to support the missions for attitude determination, prediction, and control. In addition, descriptions of the operating procedures which will be used to accomplish mission objectives are provided.

Werking, R. D.↗