SEARCH · Search NASA

Results for “autonomous control system”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Experimental validation of a high fidelity Monte Carlo neutron transport model of the MIT graphite exponential pile

High-fidelity modeling and simulation were performed for the MIT graphite exponential pile (MGEP) using Monte Carlo neutron transport codes OpenMC and MCNP, and the results were validated by experimental data. The MGEP is being used as the test bed for the design of an autonomous control system for the pile's neutron flux distribution. The main contribution of this work is to generate the training data sets of neutron flux distributions with different locations of control rods that perturb the neutron flux profiles. First, code -to-code cross verification between OpenMC and MCNP was performed to ensure consistency of the numerical modeling within statistical uncertainties. To validate the accuracy of this high-fidelity model, a series of neutron flux measurements were conducted using a Helium-3 (He-3) neutron detector on a mobile platform that is placed inside the pile. Second, the neutron flux profiles were measured in four vertical layers of interest, and compared to the corresponding simulation results. The comparison results shows that the root mean square error is less than 2.5% in the two upper layers, and less than 4.5% in all four measured layers. Here the results validated the accuracy of the modeling and simulation. Finally, the relative change of the neutron flux profiles from moving control rods was analyzed, which identified the layer that has the best sensitivity regarding the control rods movements. Thus, this work identified and provided training data sets of both simulated and experimental neutron flux profiles in the most sensitive layer, paving the path forward to the real-time experimental demonstration of the autonomous control system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Spatially Adaptive Tunable Lighting Control System with Expanded Wellness and Energy Saving Benefits

Lighting design is becoming increasingly complex, including active dimming for energy savings and spectral tuning for human wellbeing. Modern commercial lighting control systems are already difficult to use, and maintain, and even with so-called Smart Lighting, optimizing light settings is rapidly exceeding the capabilities of direct human control, limiting the adoption of lighting control systems. The growing importance of Occupancy Centric Controls (OCC) for advanced building management systems can be applied to the development of autonomous lighting system controls needed to drive the adoption of advanced lighting controls for improved adaptive “sculpted illumination” for both greater energy savings and broader human wellbeing perspectives. This project, led by Rensselaer Polytechnic Institute, entitled “Spatially adaptive tunable lighting control system with expanded wellness and energy saving benefits” will develop and test an entirely new platform for automated optimized lighting design and control that requires little or no human engagement, yet contours lighting profiles automatically to minimize lighting energy use. We call this approach to lighting control “dynamic light sculpting” since the right amount of illumination is automatically delivered to occupants in real time. Since the system uses new, privacy-preserving occupant position and pose detection technologies developed by Rensselaer for broad OCC building applications, the control system will analyze how to deliver the right amount of the right type of illumination only where and when it is needed based these OCC platforms. To create these powerful autonomous lighting control platforms, the project will integrate evolving augmented reality (AR) and virtual reality (VR) tools with sophisticated lighting and interior design toolkits to create interactive lighting design and control simulators. Using the quickly growing paradigm of digital twins, these tools will integrate light fixture properties, occupancy sensor data, interior design data, and lighting specifications to accurately visualize how various design concepts interact with simulated yet realistic human activities that occur in commercial office buildings of various types. These advanced digital twin design and simulation tools will be combined with a new class of digitally-programmable LED lighting fixtures that can dynamically change the spectral content and direction of light emission. When fully integrated, the new autonomous lighting control system will take all of the guesswork out of optimizing light quality while minimizing energy consumption. Digital twin tools will simplify the design, installation, commissioning, operation, and maintenance of future energy-efficient lighting systems. It should be possible to reduce lighting energy costs from 40% to 70% with OCC based dynamic light sculpting systems. The work will be led by the Center for Lighting Enabled Systems and Applications (LESA) and the Center for Architectural Science and Ecology (CASE), both at Renssealer Polytechnic Institute; Lumileds, a global leader in the development of advanced LED systems; and HKS, a leading global architecture design firm. This interdisciplinary project team’s experience includes all of the design simulation tools, VR/AR digital twin visualization technology, advanced occupancy sensing technology, and complex control system design capabilities that will revolutionize lighting design and control technology to autonomously deliver high-quality lighting that improves human health and wellbeing while simultaneously maximizing lighting energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control

In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Predicting Pulsed-Laser Deposition SrTiO 3 Homoepitaxy Growth Dynamics Using High-Speed Reflection High-Energy Electron Diffraction

Pulsed-laser deposition (PLD) is a powerful technique for growing complex oxides with controlled stoichiometry. To understand growth dynamics therein, it is common to leverage in situ spectroscopies, such as reflection high-energy electron diffraction (RHEED), to monitor surface crystallinity. Most commercial systems rely on video-rate cameras operating at 60-120 Hz that lack sufficient temporal resolution to capture growth dynamics at practical deposition frequencies. Here, a high-speed platform to record in situ dynamics via RHEED at >500 Hz is implemented. An open-source analysis package is designed to fit diffraction spots to 2D Gaussians, allowing single-pulse surface reconstruction kinetics extraction. Using homoepitaxially deposited (001)-oriented SrTiO 3 as a model system, we demonstrate how high-speed RHEED can provide real-time insight into growth processes obscured by slower acquisition systems. By fitting the single-pulse intensity to a set of exponential functions, we observe changes in the characteristic decay time and mechanism correlated to the substrate step width and surface termination. We observe distinct surface effects, with diffraction intensity decaying on lower-energy TiO 2 -terminated surfaces and stabilizing on SrO- or mixed-terminated surfaces. Similarly, using an exponential model, the extracted characteristic time of adatom deposition decreases with increased density of bonding sites associated with mixed termination and narrower step widths. Ultimately, this work shows how increasing RHEED temporal resolution can uncover new insights into growth processes, with practical implications for the design and control of PLD processes. This experimental platform provides new capabilities to enable data-driven machine learning analysis and autonomous control systems to enhance the complexity and fecundity of PLD.

(SrO)↗

DOE OSTI2023

Design and Stability Analysis of Control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS)

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system.

Xia, Qian↗

DOE OSTI2021

Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review

Not Available

Lin, Linyu↗

DOE OSTI2021

Development of a Nearly Autonomous Management and Control (NAMAC) System for Advanced Reactors (Final Report for ARPA-E Project)

The project formulates a technical basis for, and preliminary development of, a Nearly Autonomous Management and Control (NAMAC) system in advanced reactors. The system is intended to provide recommendations to operators during plant operation. The NAMAC system utilizes simulation-informed, data/knowledge-driven, AI-guided, plant-data-assimilated real-time operating procedures to effectively support the operator in risk-informed management of plant operations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Creation of Laboratory-scale Testbed for Autonomous Monitoring and Control of Subsurface Systems

This report describes the results of an LDRD project aiming to develop a laboratory-scale testbed for autonomous subsurface monitoring and control systems. Modifications were made to an existing polyaxial loading frame to incorporate electrical resistivity tomography (ERT) and acoustic emission (AE) monitoring systems. This allows engineered subsurface processes to be studied under realistic temperatures and stresses in a scaled-down laboratory environment with similar monitoring systems as are deployed in field-scale systems. The paper outlines some challenges that were faced in implementing the ERT system at the laboratory scale and the solutions that were devised. Results are shown from two demonstration tests—one focusing on the ERT system, and one focusing on the AE system.

42 ENGINEERING↗

DOE OSTI2026

Constellation: The autonomous control and data acquisition system for dynamic experimental setups

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Autonomy↗

DOE OSTI2021

Control systems and methods to enable autonomous drilling

Buerger, Stephen↗

DOE OSTI2022

Control systems and methods to enable autonomous drilling

A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Buerger, Stephen↗

DOE OSTI2023

Control systems and methods to enable autonomous drilling

Buerger, Stephen↗

DOE OSTI2023

Control oriented model order reduction for the hydraulic system of an autonomous wheel loader

Control design for an autonomous wheel loader requires a high-fidelity and low-order dynamics model. The objective of order reduction is to decrease the number of states in the model while maintaining performance comparable to the original model. Of all the power components in a wheel loader, the model order reduction for steering and working hydraulic systems is most demanding due to its nonlinear and stiff dynamical nature. This paper describes a physics-inspired model order reduction method that can reduce the model order by close to thirty percent. It is achieved by discarding orders with fast dynamics and consolidating multiple states into less new states. Furthermore, this method can also be extended to other types of off-road vehicles such as excavators, bulldozers, etc. Simulation results demonstrate that the reduced-order model exhibits very similar performance to the full-order model under typical operating conditions of wheel loaders, with an output error of less than six percent.

42 ENGINEERING↗

DOE OSTI2022

Crowd Dynamics: Modeling and Control of Multiagent Systems

This review aims to present recent developments in modeling and control of multiagent systems. A particular focus is set on crowd dynamics characterized by complex interactions among agents, also called social interactions, and large-scale systems. Specifically, in a crowd each individual agent interacts with a field generated by the other agents and the environment. These systems can be modeled at the microscopic scale by ordinary differential equations, while an alternative description at the mesoscopic scale is given by a partial differential equation for the propagation of the probability density of the agents. Control actions can be applied at the individual level as well as at the level of the corresponding fields. This article presents and compares different control types, and the specific application to multilane, multiclass traffic is developed in some detail, showing the main tools at work in a hybrid setting with relevant impacts on autonomous driving.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Control System of Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS) & HIL Platforms for Design

Multi-port autonomous reconfigurable solar power plant (MARS) provides an attractive alternative to connect photovoltaic (PV) and energy storage systems (ESSs) to high-voltage direct current (HVdc) links and high-voltage alternating current (ac) grids. In this paper, a unique hierarchical control system of MARS is proposed and evaluated. To evaluate the control system and associated algorithms in early-stage research of complex architectures like MARS, it is important to develop unique suitable hardware-in-the-loop (HIL) platforms. In this paper, the HIL platforms for MARS to evaluate the performance of the hierarchical control system and the control algorithms implemented are presented. Further, they help with the design process of control systems. The real-time simulation models and algorithms that are utilized for MARS in the HIL platforms are also discussed in the paper. The HIL experiments of the control system of MARS showcase the capability to provide continuity of operation under faults and frequency support to the power grid during loss of generation. They also showcase the stability of the proposed hierarchical control system of MARS.

14 SOLAR ENERGY↗

DOE OSTI2023

Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frame-works), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows.

Castellana, Vito G.↗

DOE OSTI2024

Towards Automated Generation of Chiplet-Based Systems

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application- Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a high-level frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frameworks), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This talk will discuss ongoing work on the SODA synthesizer to enable no-human-in-the-loop generation and design space exploration of the chiplets for highly specialized artificial intelligence accelerators. Connecting these highly specialized chiplets to general-purpose cores or programmable accelerators will allow to quickly deploy autonomous systems for scientific discovery.

Limaye, Ankur M.↗