Search NASA⌕ Search

SEARCH · Search NASA

Results for “Automation & control systems”

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.

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

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

An approach to automated longwall mining

The longwall system of mining coal, providing advantages in the areas of productivity as well as health and safety, is described, and technological developments leading to a full automation of the system are discussed. In the longwall system large blocks of coal (up to 600 feet wide and up to 5000 feet long) are developed, with each block mined out by taking successive slices across the short dimension of the block and loading the broken coal onto a conveyor. A self-advancing system supports the roof over the length of the face throughout cutting and loading, with the supports advanced with the face, and the roof allowed to collapse behind them. A double-ranging drum longwall shearer provides the system with an efficient yaw, roll, and variable-thickness vertical control. Currently two machine operators function as error detectors and controllers. It is shown that electronic sensors can lead to a fully automated vertical control system, and automatic roll control is achievable with available instruments and machine tilt actuators.

Palowitch, E. R.↗
NASA NTRS1993

Performance consequences of automation-induced 'complacency'

The effect of variations in the reliability of an automated monitoring system on human operator detection of automation failures was examined in two experiments. For four 30-min sessions, 40 subjects performed an IBM PC-based flight simulation that included manual tracking and fuel-management tasks, as well as a system-monitoring task that was under automation control. Automation reliability - the percentage of system malfunctions detected by the automation routine - either remained constant at a low or high level over time or alternated every 10 min from low to high. Operator detection of automation failures was substantially worse for constant-reliability than for variable-reliability automation after about 20 min under automation control, indicating that the former condition induced 'complacency'. When system monitoring was the only task, detection was very efficient and was unaffected by variations in automation reliability. The results provide the first empirical evidence of the performance consequences of automation-induced 'complacency'. We relate findings to operator attitudes toward automation and discuss implications for cockpit automation design.

Parasuraman, Raja↗
DOE OSTI2020

Lab-Scale, Closed-Loop Experimental Characterization, Model Refinement, and Validation of a Hydrokinetic Energy-Harvesting Ocean Kite

This paper presents a study wherein we experimentally characterize the dynamics and control system of a lab-scale ocean kite, and then refine, validate, and extrapolate this model for use in a full-scale system. Ocean kite systems, which harvest tidal and ocean current resources through high-efficiency cross-current motion, enable energy extraction with an order of magnitude less material (and cost) than stationary systems with the same rated power output. However, an ocean kite represents a nascent technology that is characterized by relatively complex dynamics and requires sophisticated control algorithms. In order to characterize the dynamics and control of ocean kite systems rapidly, at a relatively low cost, the authors have developed a lab-scale, closed-loop prototyping environment for characterizing tethered systems, whereby 3D printed systems are tethered and flown in a water channel environment. While this system has been shown to be capable of yielding similar dynamic characteristics to some full-scale systems, there are also fundamental limitations to the geometric scales and flow speeds within the water channel environment, making many other real-world scenarios impossible to replicate from the standpoint of dynamic similarity. Here, to address these scenarios, we show how the lab-scale framework is used to refine and validate a scalable dynamic model of a tethered system, which can then be extrapolated to full-scale operation. In this work, we present an extensive case study of this model refinement, validation, and extrapolation on an ocean kite system intended for operation in the Gulf Stream or similar current environments.

42 ENGINEERING↗
DOE OSTI2025

Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1996

A Framework for Modeling Human-Machine Interactions

Modern automated flight-control systems employ a variety of different behaviors, or modes, for managing the flight. While developments in cockpit automation have resulted in workload reduction and economical advantages, they have also given rise to an ill-defined class of human-machine problems, sometimes referred to as 'automation surprises'. Our interest in applying formal methods for describing human-computer interaction stems from our ongoing research on cockpit automation. In this area of aeronautical human factors, there is much concern about how flight crews interact with automated flight-control systems, so that the likelihood of making errors, in particular mode-errors, is minimized and the consequences of such errors are contained. The goal of the ongoing research on formal methods in this context is: (1) to develop a framework for describing human interaction with control systems; (2) to formally categorize such automation surprises; and (3) to develop tests for identification of these categories early in the specification phase of a new human-machine system.

Shafto, Michael G.↗
NASA NTRS2010

High-resolution, continuous field-of-view (FOV), non-rotating imaging system

A high resolution CMOS imaging system especially suitable for use in a periscope head. The imaging system includes a sensor head for scene acquisition, and a control apparatus inclusive of distributed processors and software for device-control, data handling, and display. The sensor head encloses a combination of wide field-of-view CMOS imagers and narrow field-of-view CMOS imagers. Each bank of imagers is controlled by a dedicated processing module in order to handle information flow and image analysis of the outputs of the camera system. The imaging system also includes automated or manually controlled display system and software for providing an interactive graphical user interface (GUI) that displays a full 360-degree field of view and allows the user or automated ATR system to select regions for higher resolution inspection.

Huntsberger, Terrance L.↗
DOE OSTI2021

Energy Management Information Systems Technical Resources Report

Guide supports federal facility staff in understanding, designing, procuring, and implementing Energy Management Information Systems (EMIS) as a valuable component of their portfolio-level energy and water planning and management strategies. As a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance, EMIS tools present significant opportunities for federal sector energy savings and improved operational performance. EMIS are at the forefront of transforming energy management best practices by providing building owners and operators with well-organized building performance and energy consumption data, enabling a host of analytic capabilities. These capabilities include portfolio-wide energy benchmarking, data visualization, and key performance indicator tracking; automated fault detection and diagnostics (AFDD); artificial intelligence for predictive analytics and control; automated measurement and verification of energy conservation measures; and supervisory control enabling automated system optimization and demand management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗
NASA NTRS1993

Automatic tools for system testing

As spacecraft control and other space-related ground systems become increasingly complex, the effort required in testing and validation also increases. Implementation of a spacecraft control system normally involves a number of incremental deliveries. In addition kernel or general purpose software may also be involved, which must itself be considered in the integration and testing program. Tools can be used to assist this testing. These can reduce the effort required or alternatively they can ensure that for a given level of effort, a better job is done. Great benefit could be derived by automating certain types of testing (interactive software) which up to now has been performed manually at a terminal. This paper reports on an on-going study. The study examines means of automating spacecraft control system testing, evaluates relevant commercial tools and aims to prototype basic automatic testing functions.

Peccia, N. M.↗
DOE OSTI2024

"Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test"

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Jang, Kathy↗
NASA NTRS1990

Automation of the Environmental Control and Life Support System

The objective of the Environmental Control and Life Support System (ECLSS) Advanced Automation Project is to recommend and develop advanced software for the initial and evolutionary Space Station Freedom (SSF) ECLS system which will minimize the crew and ground manpower needed for operations. Another objective includes capturing ECLSS design and development knowledge for future missions. This report summarizes our results from Phase I, the ECLSS domain analysis phase, which we broke down into three steps: 1) Analyze and document the baselined ECLS system, 2) envision as our goal an evolution to a fully automated regenerative life support system, built upon an augmented baseline, and 3) document the augmentations (hooks and scars) and advanced software systems which we see as necessary in achieving minimal manpower support for ECLSS operations. In addition, Phase I included development of an advanced software life cycle testing tools will be used in the development of the software. In this way, we plan in preparation for phase II and III, the development and integration phases, respectively. Automated knowledge acquisition, engineering, verification, and can capture ECLSS development knowledge for future use, develop more robust and complex software, provide feedback to the KBS tool community, and insure proper visibility of our efforts.

Dewberry, Brandon S.↗
DOE OSTI2023

Mean-field limit of a hybrid system for multi-lane multi-class traffic

This article aims to study coupled mean-field equation and ODEs with discrete events motivated by vehicular traffic flow. Precisely, multi-lane traffic flow in presence of human-driven and autonomous vehicles is considered, with autonomous vehicles possibly influenced by external policymakers. First, a finite-dimensional hybrid system is developed based on the continuous Bando-Follow-the-Leader dynamics coupled with discrete events due to lane-change maneuvers. Then the mean-field limit of the finite-dimensional hybrid system is rigorously derived for the dynamics of the human-driven vehicles. The microscopic lane-change maneuvers of the human-driven vehicles generate a source term for the mean-field PDE. This leads to an infinite-dimensional hybrid system described by coupled Vlasov-type PDE, ODEs, and discrete events.

97 MATHEMATICS AND COMPUTING↗