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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 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Adaptive Internal Model Based Current Control with Embedded Active Damping of a Three-Phase Grid-Connected Inverter with LCL Filter for PV Application

This paper presents an internal model based controller with embedded active damping for a three-phase grid-connected inverter with LCL filter. A strategy to control the grid current based on the internal model principle is presented in this paper. It is known that a third-order LCL filter apart from providing better attenuation also poses the problem of initiating unwanted resonance oscillations especially during transients. To address this problem, an active damping strategy based on virtual resistance emulation on the grid side filter is implemented. Also, to ensure effective active damping, an online parameter update strategy is implemented for the filter parameters. The proposed method provides better attenuation to oscillation damping and achieves good tracking performance without adversely affecting the bandwidth. The architecture is verified based on computer simulations via MATLAB/Simulink and PLECS domain and various important case study results are presented.

grid-connected inverter↗

DOE OSTI2026

Single-Phase to Split-Phase Inverters with Advanced Grid Support Functions for Grid-Interactive Applications

This work presents a cost-effective single-phase to split-phase inverter with a reduced switch count, achieving grid interactive performance while maintaining operational efficiency. The proposed system integrates an Andronov-Hopf oscillator based secondary controller, which inherently embeds a nonlinear resistive droop architecture, ensuring rapid dynamic response. A Lyapunov energy function-based primary control enhances transient stability and regulation, while an internal model-based point of common coupling voltage estimation enables cost optimization without additional sensors. Equipped with advanced grid support functionalities, the inverter facilitates seamless distribution system operation with enhanced robustness. The effectiveness of the proposed architecture and control strategy is validated through MATLAB/Simulink and PLECS simulations, demonstrating its feasibility for high-performance grid-supportive applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Impact of Blockchain Delay on Grid-Tied Solar Inverter Performance

This paper investigates the impact of the delay resulting from a blockchain, a promising security measure, for a hierarchical control system of inverters connected to the grid. The blockchain communication network is designed at the secondary control layer for resilience against cyberattacks. To represent the latency in the communication channel, a model is developed based on the complexity of the blockchain framework. Taking this model into account, this work evaluates the plant’s performance subject to communication delays, introduced by the blockchain, among the hierarchical control agents. In addition, this article considers an optimal model-based control strategy that performs the system’s internal control loop. The work shows that the blockchain’s delay size influences the convergence of the power supplied by the inverter to the reference at the point of common coupling. In the results section, real-time simulations on OPAL-RT are performed to test the resilience of two parallel inverters with increasing blockchain complexity.

41 EE - Solar Energy Technologies Office (EE-4S)↗

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↗

NASA NTRS1997

A Method for Generating Reduced Order Linear Models of Supersonic Inlets

For the modeling of high speed propulsion systems, there are at least two major categories of models. One is based on computational fluid dynamics (CFD), and the other is based on design and analysis of control systems. CFD is accurate and gives a complete view of the internal flow field, but it typically has many states and runs much slower dm real-time. Models based on control design typically run near real-time but do not always capture the fundamental dynamics. To provide improved control models, methods are needed that are based on CFD techniques but yield models that are small enough for control analysis and design.

Chicatelli, Amy↗

NASA NTRS2013

Model-based Robotic Dynamic Motion Control for the Robonaut 2 Humanoid Robot

Robonaut 2 (R2), an upper-body dexterous humanoid robot, has been undergoing experimental trials on board the International Space Station (ISS) for more than a year. R2 will soon be upgraded with two climbing appendages, or legs, as well as a new integrated model-based control system. This control system satisfies two important requirements; first, that the robot can allow humans to enter its workspace during operation and second, that the robot can move its large inertia with enough precision to attach to handrails and seat track while climbing around the ISS. This is achieved by a novel control architecture that features an embedded impedance control law on the motor drivers called Multi-Loop control which is tightly interfaced with a kinematic and dynamic coordinated control system nicknamed RoboDyn that resides on centralized processors. This paper presents the integrated control algorithm as well as several test results that illustrate R2's safety features and performance.

Badger, Julia M.↗

DOE OSTI2021

Impact of Blockchain Delay on Grid-Tied Solar Inverter Performance: Preprint

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Kimberlina 1.2 Velocity Models and Seismic Data

Kimberlina 1.2 Velocity model and synthetic seismic data, produced in collaboration of teams at the National Energy Technology Laboratory, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory through the National Risk Assessment Partnership. Data is associated with the following publication: Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019. The Kimberlina 1.2 Velocity models were produced by Zan Wang, Robert Dilmore, William Harbert, and Lianjie Huang at NETL. The following citations are directly related to the creation of the velocity models: Wang, Z. Harbert, W., Dilmore, R., Huang, L. Modeling of time-lapse seismic monitoring using CO2 leakage simulations for a model CO2 storage site with realistic geology: Application in assessment of early leak-detection capabilities. International Journal of Greenhouse Gas Control. V. 76, September 2018, Pages 39-52. https://doi.org/10.1016/j.ijggc.2018.06.011 Wang, Z., Dilmore, R., Harbert, W. Inferring CO2 saturation from synthetic surface seismic and downhole monitoring data using machine learning for leakage detection at CO2 sequestration sites. International Journal of Greenhouse Gas Control, V. 100, September 2020. https://doi.org/10.1016/j.ijggc.2020.103115 The velocity models were built based on the Kimberlina 1.2 aquifer impact data which is associated with the following publications: Buscheck, T.A., Mansoor, K., Yang, X., Wainwright, H., and Carroll, S. (2019). Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir. International Journal of Greenhouse Gas Control. 91. 102812. 10.1016/j.ijggc.2019.102812. Xianjin Yang, Thomas A. Buscheck, Kayyum Mansoor, Zan Wang, Kai Gao, Lianjie Huang, Delphine Appriou, Susan A. Carroll, Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, Volume 90, 2019, 102803, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2019.102803 The synthetic seismic data was produced by Youzuo Lin and team at LANL, and are associated with the following citations: Jordan, P. D., and J. L. Wagoner. Characterizing Construction of Existing Wells to a CO2 Storage Target: The Kimberlina Site, California. Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019.

CO2 Leakage↗

NASA NTRS1999

Design of the Wind Tunnel Model Communication Controller Board

The NASA Langley Research Center's Wind Tunnel Reinvestment project plans to shrink the existing data acquisition electronics to fit inside a wind tunnel model. Space limitations within a model necessitate a distributed system of Application Specific Integrated Circuits (ASICs) rather than a centralized system based on PC boards. This thesis will focus on the design of the prototype of the communication Controller board. A portion of the communication Controller board is to be used as the basis of an ASIC design. The communication Controller board will communicate between the internal model modules and the external data acquisition computer. This board is based around an Field Programmable Gate Array (FPGA), to allow for reconfigurability. In addition to the FPGA, this board contains buffer Random Access Memory (RAM), configuration memory (EEPROM), drivers for the communications ports, and passive components.

Wilson, William C.↗

NASA NTRS1979

A rationale for human operator pulsive control behavior

When performing tracking tasks which involve demanding controlled elements such as those with K/s-squared dynamics, the human operator often develops discrete or pulsive control outputs. A dual-loop model of the human operator is discussed, the dominant adaptive feature of which is the explicit appearance of an internal model of the manipulator-controlled element dynamics in an inner feedback loop. Using this model, a rationale for pulsive control behavior is offered which is based upon the assumption that the human attempts to reduce the computational burden associated with time integration of sensory inputs. It is shown that such time integration is a natural consequence of having an internal representation of the K/s-squared-controlled element dynamics in the dual-loop model. A digital simulation is discussed in which a modified form of the dual-loop model is shown to be capable of producing pulsive control behavior qualitively comparable to that obtained in experiment.

Hess, R. A.↗

DOE OSTI2022

Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys

Mechanical properties of alloys are controlled by their microstructure; and microstructure evolution is controlled by internal and external stressors. Chemical complexity during the alloy processing may result in heterogeneity and observation of multimodal performance patterns. Thus, to ensure the desired performance of stressed components it is important to understand the origin and mechanisms of such behavior. Adversarial ensemble modeling was introduced here to explain multimodal mechanical properties of the iron-based alloys. The modeling results showed that the areas of a single mechanism predominance were contiguous across the alloy compositions clustered by similarity, with sharp and persistent boundaries separating the single-mechanism domains. The marginal compositions resulted in increased competition between the adversarial models but only led to the multimodal behavior when the competing models diverged. Transparency of the clustering method allowed explicit interpretation of the chemistries leading to such competition. The adversarial ensemble was interpreted through transition from ductile to brittle microstructure.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems

Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.

AI-based models↗

NASA NTRS1993

ESSOPE: Towards S/C operations with reactive schedule planning

The ESSOPE is a prototype front-end tool running on a Sun workstation and interfacing to ESOC's MSSS spacecraft control system for the exchange of telecommand requests (to MSSS) and telemetry reports (from MSSS). ESSOPE combines an operations Planner-Scheduler, with a Schedule Execution Control function. Using an internal 'model' of the spacecraft, the Planner generates a schedule based on utilization requests for a variety of payload services by a community of Olympus users, and incorporating certain housekeeping operations. Conflicts based on operational constraints are automatically resolved, by employing one of several available strategies. The schedule is passed to the execution function which drives MSSS to perform it. When the schedule can no longer be met, either because the operator interferes (by delays or changes of requirements), or because ESSOPE has recognized some spacecraft anomalies, the Planner produces a modified schedule maintaining the on-going procedures as far as consistent with the new constraints or requirements.

Wheadon, J.↗

NASA NTRS1990

Space-time modeling using environmental constraints in a mobile robot system

Grid-based models of a robot's local environment have been used by many researchers building mobile robot control systems. The attraction of grid-based models is their clear parallel between the internal model and the external world. However, the discrete nature of such representations does not match well with the continuous nature of actions and usually serves to limit the abilities of the robot. This work describes a spatial modeling system that extracts information from a grid-based representation to form a symbolic representation of the robot's local environment. The approach makes a separation between the representation provided by the sensing system and the representation used by the action system. Separation allows asynchronous operation between sensing and action in a mobile robot, as well as the generation of a more continuous representation upon which to base actions.

Slack, Marc G.↗

NASA NTRS2003

Applying Model-Based Reasoning to the FDIR of the Command and Data Handling Subsystem of the International Space Station

All of the International Space Station (ISS) systems which require computer control depend upon the hardware and software of the Command and Data Handling System (C&DH) system, currently a network of over 30 386-class computers called Multiplexor/Dimultiplexors (MDMs)[18]. The Caution and Warning System (C&W)[7], a set of software tasks that runs on the MDMs, is responsible for detecting, classifying, and reporting errors in all ISS subsystems including the C&DH. Fault Detection, Isolation and Recovery (FDIR) of these errors is typically handled with a combination of automatic and human effort. We are developing an Advanced Diagnostic System (ADS) to augment the C&W system with decision support tools to aid in root cause analysis as well as resolve differing human and machine C&DH state estimates. These tools which draw from sources in model-based reasoning[ 16,291, will improve the speed and accuracy of flight controllers by reducing the uncertainty in C&DH state estimation, allowing for a more complete assessment of risk. We have run tests with ISS telemetry and focus on those C&W events which relate to the C&DH system itself. This paper describes our initial results and subsequent plans.

Robinson, Peter↗

DOE OSTI2023

Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control

Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.

Zhou, Anye↗

DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗

DOE OSTI2024

Deep Learning-Based Dynamic Modeling of Three-Phase Voltage Source Inverters

Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.

deep learning, artificial intelligence↗