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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Real-Time Control of Lean Blowout in a Turbine Engine for Minimizing No(x) Emissions

This report describes research on the development and demonstration of a controlled combustor operates with minimal NO, emissions, thus meeting one of NASA s UEET program goals. NO(x) emissions have been successfully minimized by operating a premixed, lean burning combustor (modeling a lean prevaporized, premixed LPP combustor) safely near its lean blowout (LBO) limit over a range of operating conditions. This was accomplished by integrating the combustor with an LBO precursor sensor and closed-loop, rule-based control system that allowed the combustor to operate far closer to the point of LBO than an uncontrolled combustor would be allowed to in a current engine. Since leaner operation generally leads to lower NO, emissions, engine NO, was reduced without loss of safety.

Zinn, Ben↗
NASA NTRS2012

Design and Implementation of the PALM-3000 Real-Time Control System

This paper reflects, from a computational perspective, on the experience gathered in designing and implementing realtime control of the PALM-3000 adaptive optics system currently in operation at the Palomar Observatory. We review the algorithms that serve as functional requirements driving the architecture developed, and describe key design issues and solutions that contributed to the system's low compute-latency. Additionally, we describe an implementation of dense matrix-vector-multiplication for wavefront reconstruction that exceeds 95% of the maximum sustained achievable bandwidth on NVIDIA Geforce 8800GTX GPU.

PALM-3000↗
DOE OSTI2026

A New Era for Modern Bubble Chamber Technology: Cooling and Real-Time Control for the MAMBA Neutrino Bubble Chamber∗

Future neutrino experiments such as DUNE will be limited less by statistics than by how well neutrino--nucleus interactions are understood, and the cleanest way to improve that understanding is to measure interactions with light nuclei such as hydrogen or deuterium. The detector best suited to the job, the bubble chamber, has not been built for a neutrino beam in about fifty years. MAMBA (Modern Adaptive Modular Bubble chamber Archetype) is a small prototype at Fermilab intended to bring the technology back with modern cryogenics and automation, cycling continuously at 1~Hz. This paper summarizes my work on two of its subsystems during a summer internship. A new solid copper thermal link brought the coldhead to 21.3~K in a commissioning cooldown, near the 20~K operating target. An Industrial Shields Raspberry Pi programmable logic controller (PLC) running OpenPLC was characterized at a median round-trip response of 0.64~ms over 20,000 trials, with 0.66\% of trials exceeding 1~ms. Both results support continuous cycling.

Williams, Nicholas [Fermilab; DuPage Coll.]↗
NASA NTRS1982

A unified method for evaluating real-time computer controllers: A case study

A real time control system consists of a synergistic pair, that is, a controlled process and a controller computer. Performance measures for real time controller computers are defined on the basis of the nature of this synergistic pair. A case study of a typical critical controlled process is presented in the context of new performance measures that express the performance of both controlled processes and real time controllers (taken as a unit) on the basis of a single variable: controller response time. Controller response time is a function of current system state, system failure rate, electrical and/or magnetic interference, etc., and is therefore a random variable. Control overhead is expressed as a monotonically nondecreasing function of the response time and the system suffers catastrophic failure, or dynamic failure, if the response time for a control task exceeds the corresponding system hard deadline, if any. A rigorous probabilistic approach is used to estimate the performance measures. The controlled process chosen for study is an aircraft in the final stages of descent, just prior to landing. First, the performance measures for the controller are presented. Secondly, control algorithms for solving the landing problem are discussed and finally the impact of the performance measures on the problem is analyzed.

Shin, K. G.↗
NASA NTRS1985

A unified method for evaluating real-time computer controllers and its application

A real time control system consists of a synergistic pair, that is, a controlled process and a controller computer. Performance measures for real time controller computers are defined on the basis of the nature of this synergistic pair. A case study of a typical critical controlled process is presented in the context of new performance measures that express the performance of both controlled processes and real time controllers (taken as a unit) on the basis of a single variable: controller response time. Controller response time is a function of current system state, system failure rate, electrical and/or magnetic interference, etc., and is therefore a random variable. Control overhead is expressed as a monotonically nondecreasing function of the response time and the system suffers catastrophic failure, or dynamic failure, if the response time for a control task exceeds the corresponding system hard deadline, if any. A rigorous probabilistic approach is used to estimate the performance measures. The controlled process chosen for study is an aircraft in the final stages of descent, just prior to landing. First, the performance measures for the controller are presented. Secondly, control algorithms for solving the landing problem are discussed and finally the impact of the performance measures on the problem is analyzed.

Shin, K. G.↗
NASA NTRS1992

Technology test bed engine real-time failure control

The Real-Time Failure Control (RTFC) program involves development of a failure detection algorithm, for the Space Shuttle Main Engine (SSME). This failure detection approach is signal-based and entails monitoring SSME measurement signals based on predetermined as well as on-line computed mean and standard deviation values. Twenty-four engine measurements are monitored in the algorithm and provisions are made to add more parameters if needed. Each of the first values of every measurement signal at the algorithm start is checked against safety limits placed around a pre-computed engine-to-engine mean value (MV) with a bandwidth equal to a given multiple of the pre-computed standard deviation (SD). If several parameters are out of the bounds of these limits a failure is signaled. During the first two seconds (after algorithm start) a moving average (MA) and a SD is computed on-line in real-time. The moving average of each parameter is computed by averaging the incoming signal measurement with the four most recent previous signal measurements. The moving average is updated at every sampling interval (40 msec) and is checked against a similar safety band around the initial signal value for each parameter. If several anomalies are registered, a failure is signaled by the algorithm. At the end of the two-second interval the MA is fixed as the mean value for the rest of the algorithm operation and a safety band is placed above and below this value equal to a multiple of the computed SD. However, the safety band is adjusted by adjusting the mean value when propellant tank repressurization and venting take place. 'Influence Coefficients' are used to make the necessary adjustments to the safety limits of those parameters that are affected by repressurization and venting or valve closure and opening. The MA is, in both cases, continuously updated and checked against the safety band. Once more, if several parameters exceed the limits a failure is signaled. At the start of every scheduled power transient the algorithm is stopped. It is re-initiated after two seconds from the termination of the power transient and the process is repeated. The final report is divided into four major sections. The most encompassing of all is the discussion section that has sub-sections on: (1) RTFC algorithm development, (2) RTFC simulations; (3) RTFC current limitations; and (4) enhancements planned for.

Panossian, Hagop V.↗
NASA NTRS1990

Real-time failure control (SAFD)

The Real Time Failure Control program involves development of a failure detection algorithm, referred as System for Failure and Anomaly Detection (SAFD), for the Space Shuttle Main Engine (SSME). This failure detection approach is signal-based and it entails monitoring SSME measurement signals based on predetermined and computed mean values and standard deviations. Twenty four engine measurements are included in the algorithm and provisions are made to add more parameters if needed. Six major sections of research are presented: (1) SAFD algorithm development; (2) SAFD simulations; (3) Digital Transient Model failure simulation; (4) closed-loop simulation; (5) SAFD current limitations; and (6) enhancements planned for.

Panossian, Hagop V.↗
NASA NTRS1977

Real time computer controlled weld skate

A real time, adaptive control, automatic welding system was developed. This system utilizes the general case geometrical relationships between a weldment and a weld skate to precisely maintain constant weld speed and torch angle along a contoured workplace. The system is compatible with the gas tungsten arc weld process or can be adapted to other weld processes. Heli-arc cutting and machine tool routing operations are possible applications.

Wall, W. A., Jr.↗
NASA NTRS1989

Method and apparatus for adaptive force and position control of manipulators

The present invention discloses systematic methods and apparatus for the design of real time controllers. Real-time control employs adaptive force/position by use of feedforward and feedback controllers, with the feedforward controller being the inverse of the linearized model of robot dynamics and containing only proportional-double-derivative terms is disclosed. The feedback controller, of the proportional-integral-derivative type, ensures that manipulator joints follow reference trajectories and the feedback controller achieves robust tracking of step-plus-exponential trajectories, all in real time. The adaptive controller includes adaptive force and position control within a hybrid control architecture. The adaptive controller, for force control, achieves tracking of desired force setpoints, and the adaptive position controller accomplishes tracking of desired position trajectories. Circuits in the adaptive feedback and feedforward controllers are varied by adaptation laws.

Seraji, Homayoun↗
NASA NTRS1999

Real-time Adaptive Control Using Neural Generalized Predictive Control

The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

Haley, Pam↗
NASA NTRS1990

Results of the Systems Autonomy Demonstration Project

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for real-time control and fault detection, isolation and recovery (FDIR) of a prototype two-phase Space Station Freedom external control system (TCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as nominal control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach, layering model-based expert system software on a conventional numerical data acquisition and control system. The model-based capabilities of TEXSYS were shown to be advantageous, particularly for detection of unforeseen faults and sensor failures.

Glass, B. J.↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
NASA NTRS1989

A real time, FEM based optimal control algorithm and its implementation using parallel processing hardware (transistors) in a microprocessor environment

There is an evident need to discover a means of establishing reliable, implementable controls for systems that are plagued by nonlinear and, or uncertain, model dynamics. The development of a generic controller design tool for tough-to-control systems is reported. The method utilizes a moving grid, time infinite element based solution of the necessary conditions that describe an optimal controller for a system. The technique produces a discrete feedback controller. Real time laboratory experiments are now being conducted to demonstrate the viability of the method. The algorithm that results is being implemented in a microprocessor environment. Critical computational tasks are accomplished using a low cost, on-board, multiprocessor (INMOS T800 Transputers) and parallel processing. Progress to date validates the methodology presented. Applications of the technique to the control of highly flexible robotic appendages are suggested.

Patten, William Neff↗
NASA NTRS2001

Models of an In-Situ Propellant Production Plant for Mars Exploration

An in-situ propellant production system (ISPP) is designed to make rocket fuel from chemicals in the Martian atmosphere in order to reduce the amount of materials that would need to be brought from Earth to support Mars missions. We have developed a description of a hypothetical ISPP system that we would like to make available to researchers who are interested in the problem of automatically diagnosing failures in complex NASA systems. This problem description will help researchers to investigate problems of interest to NASA. We would like to make the following material publicly available: (1) a 'common sense' model of an ISPP system; (2) low- and medium-fidelity simulations of the ISPP system written in Microsoft Excel and HCC; and (3) previously published data and diagrams concerning ISPP components. We do not believe there are any export considerations on these materials for the following reasons: (1) These models are not useful for guidance and real time control of vehicles, encrpytion, or any other software purpose categorized under the Export Control Classification Numbers; and (2) The models are very high level and would not by themselves enable real-time control of a real hardware system. The models are at the level of common sense. They capture, for example, that if a heater is turned on an increase in temperature should result(see the attached excerpt). We do not believe there is any commercial value to this material, given the low commercial demand for propellant plants on mars. We have spoken to acting Code IC Division Chief Dan Clancy, and he concurs with our desire to make these materials publicly available via a technical report.

Goodrich, Charlie↗
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↗