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NASA NTRS1989

Overview of computational control research at UT Austin

An overview of current research activities at UT Austin is presented to discuss certain technical issues in the following areas: (1) Computer-Aided Nonlinear Control Design: In this project, the describing function method is employed for the nonlinear control analysis and design of a flexible spacecraft equipped with pulse modulated reaction jets. INCA program has been enhanced to allow the numerical calculation of describing functions as well as the nonlinear limit cycle analysis capability in the frequency domain; (2) Robust Linear Quadratic Gaussian (LQG) Compensator Synthesis: Robust control design techniques and software tools are developed for flexible space structures with parameter uncertainty. In particular, an interactive, robust multivariable control design capability is being developed for INCA program; and (3) LQR-Based Autonomous Control System for the Space Station: In this project, real time implementation of LQR-based autonomous control system is investigated for the space station with time-varying inertias and with significant multibody dynamic interactions.

Bong, Wie↗

NASA NTRS1987

Control systems for autonomous operation of the Magellan spacecraft

The Magellan low cost interplanetary mission spacecraft, scheduled for flight in 1989, employs a computer-based Attitude and Articulation Control System (AACS) that interfaces with spacecraft sensors and actuators. The sensors include two attitude reference units, a star-scanner unit, two digital sun sensors, and a tachometer. The actuators encompass three reaction wheels, a total of 12 attitude control thrusters, eight attitude-control engines, a solar array drive assembly, and an input-output drive assembly. A detailed account is given of the AACS's onboard computer architecture.

Huang, Whittak H.↗

NASA NTRS2012

An Autonomous Autopilot Control System Design for Small-Scale UAVs

This paper describes the design and implementation of a fully autonomous and programmable autopilot system for small scale autonomous unmanned aerial vehicle (UAV) aircraft. This system was implemented in Reflection and has flown on the Exploration Aerial Vehicle (EAV) platform at NASA Ames Research Center, currently only as a safety backup for an experimental autopilot. The EAV and ground station are built on a component-based architecture called the Reflection Architecture. The Reflection Architecture is a prototype for a real-time embedded plug-and-play avionics system architecture which provides a transport layer for real-time communications between hardware and software components, allowing each component to focus solely on its implementation. The autopilot module described here, although developed in Reflection, contains no design elements dependent on this architecture.

Ippolito, Corey↗

NASA NTRS2019

A Distributed Hierarchical Framework for Autonomous Spacecraft Control

Future human space missions for exploring beyond low Earth orbit are in the conceptual design stage. One such mission describes a habitat in cis-lunar orbit that is visited by crew periodically, others describe missions to Mars. These missions have one important thing in common: the need for autonomy on the spacecraft. This need stems from the latency and bandwidth constraints on communications between the vehicle and ground control. A variable amount of autonomy may be necessary whether the spacecraft has crew on board or not. Spacecraft are complex systems that are engineered as a collection of subsystems. These subsystems work together to control the overall state of the spacecraft. As such, solutions that increase the autonomy of the spacecraft (called autonomous functions) should respect both the independence and interconnectedness of the spacecraft subsystems. This distributed and hierarchical approach to system monitoring and control is a key idea in the Modular Autonomous Systems Technology (MAST) framework. The MAST framework enables a component-based architecture that provides interfaces and structure to developing autonomous technologies. The framework enforces a distributed, hierarchical architecture for autonomous control systems across subsystems, systems, elements, and vehicles. An example autonomous system was implemented in this framework and tested using realistic spacecraft software and hardware simulations. This paper will discuss the framework, tests conducted, results, and future work.

Badger, Julia M.↗

NASA NTRS1973

A movable mass control system to detumble a disabled space vehicle

An internal autonomous control system to either completely detumble a spacecraft or lessen the tumbling motions until the rescue craft arrives is discussed. Such a device would become active upon loss of control. The development of a movable mass control system to convert the tumbling motions of a disabled vehicle into simple spin is presented. A simple spin state would greatly facilitate crew evacuation and final despinning by an external means. The system moves a control mass, according to a selected control law, in the acceleration environment created by the tumbling motion. By moving the mass properly, the rotational kinetic energy of the system may be increased or decreased creating simple spin states about the minimum or maximum moment of inertia axis, respectively. The control system is designed for the latter case due to its associated stability in the presence of perturbing forces.

Edwards, T. L.↗

NASA NTRS1994

Software Architecture for a Highly Autonomous Spacecraft Control System

Future space missions will continue to be scientifically and technically more ambitious, and will demand more autonomy to accomplish complex tasks in uncertain environments and in close proximity to extraterrestrial bodies.

software architecture smaller missions science obj↗

NASA NTRS1994

The 4-D approach to visual control of autonomous systems

Development of a 4-D approach to dynamic machine vision is described. Core elements of this method are spatio-temporal models oriented towards objects and laws of perspective projection in a foward mode. Integration of multi-sensory measurement data was achieved through spatio-temporal models as invariants for object recognition. Situation assessment and long term predictions were allowed through maintenance of a symbolic 4-D image of processes involving objects. Behavioral capabilities were easily realized by state feedback and feed-foward control.

Dickmanns, Ernst D.↗

NASA NTRS2000

The MAP Autonomous Mission Control System

The Microwave Anisotropy Probe (MAP) mission is the second mission in NASA's Office of Space Science low-cost, Medium-class Explorers (MIDEX) program. The Explorers Program is designed to accomplish frequent, low cost, high quality space science investigations utilizing innovative, streamlined, efficient management, design and operations approaches. The MAP spacecraft will produce an accurate full-sky map of the cosmic microwave background temperature fluctuations with high sensitivity and angular resolution. The MAP spacecraft is planned for launch in early 2001, and will be staffed by only single-shift operations. During the rest of the time the spacecraft must be operated autonomously, with personnel available only on an on-call basis. Four (4) innovations will work cooperatively to enable a significant reduction in operations costs for the MAP spacecraft. First, the use of a common ground system for Spacecraft Integration and Test (I&T) as well as Operations. Second, the use of Finite State Modeling for intelligent autonomy. Third, the integration of a graphical planning engine to drive the autonomous systems without an intermediate manual step. And fourth, the ability for distributed operations via Web and pager access.

Breed, Juile↗

NASA NTRS1988

A CLIPS prototype for autonomous power system control

The model of the system assumes a constant power source and loads (experiments) whose power demands exceed the supply. Experiments are described by their name, power consumption, time for a complete run, present status and the state of the load. The power consumption of each load is set at a constant level but can be dynamically modified by the operator. The status specifies if the experiment is running, paused, completed or failed. The state compensates for the lack of actual feedback sensor data, by signifying the stability of the load. Experiments are scheduled to keep as many running as possible with the current system limitations. A graphics oriented user interface is embedded into the rule-based system to enable an operator to easily experiment with the system.

Vezina, James M.↗

NASA NTRS1986

Electromechanical Turboprop-Pitch-Control Mechanism

Propeller-control system autonomous and tolerant of failure. Mounting electrical-power module and conditioning/control systems inboard rotating propeller hub eliminates failure-prone slipring devices and creates autonomous, failure-tolerant propeller-control system. Modular component design facilitates on-the-wing maintenance. System highly adaptive to various sizes and gearbox configurations. Features and capabilities described unmatched by any comparable PCM now in existence. These capabilities needed by large, fuel-efficient, commuter turboprop aircraft now being developed by aircraft industry.

Steinetz, B. M.↗

NASA NTRS2017

Modular Autonomous Systems Technology Framework: A Distributed Solution for System Monitoring and Control

The Modular Autonomous Systems Technology (MAST) framework is a tool for building distributed, hierarchical autonomous systems. Originally intended for the autonomous monitoring and control of spacecraft, this framework concept provides support for variable autonomy, assume-guarantee contracts, and efficient communication between subsystems and a centralized systems manager. MAST was developed at NASA's Johnson Space Center (JSC) and has been applied to an integrated spacecraft example scenario.

Badger, Julia M.↗

NASA NTRS2002

Results of NASA's First Autonomous Formation Flying Experiment: Earth Observing-1 (EO-1)

NASA's first autonomous formation flying mission completed its primary goal of demonstrating an advanced technology called enhanced formation flying. To enable this technology, the Flight Dynamics Analysis Branch at the Goddard Space Flight Center implemented a universal 3-axis formation flying algorithm in an autonomous executive flight code onboard the New Millennium Program's (NMP) Earth Observing-1 (EO-1) spacecraft. This paper describes the mathematical background of the autonomous formation flying algorithm, the onboard flight design and the validation results of this unique system. Results from fully autonomous maneuver control are presented as comparisons between the onboard EO-1 operational autonomous control system called AutoCon, its ground-based predecessor used in operations, and the original standalone algorithm. Maneuvers discussed encompass reactionary, routine formation maintenance, and inclination control. Orbital data is also examined to verify that all formation flying requirements were met.

Folta, David↗

NASA NTRS2002

Results Of NASA's First Autonomous Formation Flying Experiment: Earth Observing-1 (EO-1)

NASA's first autonomous formation flying mission completed its primary goal of demonstrating an advanced technology called Enhanced Formation Flying. To enable this technology, a team at the Goddard Space Flight Center implemented a universal 3-axis formation flying algorithm in an autonomous executive flight code onboard the New Millennium Program's (NMP) Earth Observing-1 (EO-1) spacecraft. This paper describes the mathematical background of the autonomous formation flying algorithm, the onboard flight design and the validation results of this unique system. Results from fully autonomous maneuver control are presented as comparisons between the onboard EO-1 operational autonomous control system called AutoCon(trademark), its ground-based predecessor used in operations, and the original standalone algorithm. Maneuvers discussed encompass reactionary, routine formation maintenance, and inclination control. Orbital data is also examined to verify that all formation flying requirements were met.

Folta, David↗

NASA NTRS1987

Teaching artificial neural systems to drive: Manual training techniques for autonomous systems

A methodology was developed for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions taken. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the networks assimilates training data as the expert performs its day-to-day tasks. To demonstrate these methods, an ANS network was trained to drive a vehicle through simulated freeway traffic.

Shepanski, J. F.↗

NASA NTRS2005

Beyond the Prototype: The Design Evolution of a Deployed AI System

Our previous experiences with deployed intelligent control agents for NASA advanced life support systems (Schreckenghost et al., 2002) inspired us to develop the Distributed Collaboration and Interaction (DCI) system to help humans and mostly-autonomous software agents work together. We discovered many unaddressed needs for human interaction with control agents that operate continuously over months to years to monitor and perform process control for regenerative life support systems. These systems recover usable water or air from the waste products created by biological systems over time. Through the DCI project, we have addressed the needs for interaction between humans and autonomous control systems. This paper describes the software engineering aspects of our experiences, first, in designing and developing a prototype of the DCI system, and later, in adjusting the implementation to make the leap from initial prototype to a system ready to be applied under varying circumstances to meet different needs. The details in the paper focus primarily on the second stage of development. The DCI system is currently deployed, operating 24/7, to assist humans to interact with an advanced Water Recovery System (WRS). In addition, we have demonstrated an application of DCI to support ground personnel in mission support roles.

Martin, C.↗

NASA NTRS2004

Beyond the Prototype: The Design Evolution of a Deployed AI System

Martin, Cheryl↗

NASA NTRS2001

Preliminary Results of NASA's First Autonomous Formation Flying Experiment: Earth Observing-1 (EO-1)

NASA's first autonomous formation flying mission is completing a primary goal of demonstrating an advanced technology called enhanced formation flying. To enable this technology, the Guidance, Navigation, and Control center at the Goddard Space Flight Center has implemented an autonomous universal three-axis formation flying algorithm in executive flight code onboard the New Millennium Program's (NMP) Earth Observing-1 (EO-1) spacecraft. This paper describes the mathematical background of the autonomous formation flying algorithm and the onboard design and presents the preliminary validation results of this unique system. Results from functionality assessment and autonomous maneuver control are presented as comparisons between the onboard EO-1 operational autonomous control system called AutoCon(tm), its ground-based predecessor, and a stand-alone algorithm.

Folta, David↗

NASA NTRS2001

Results of NASA's First Autonomous Formation Flying Experiment: Earth Observing-1 (EO-1)

NASA's first autonomous formation flying mission completed its primary goal of demonstrating an advanced technology called enhanced formation flying. To enable this technology, the Guidance, Navigation, and Control center at the Goddard Space Flight Center (GSFC) implemented a universal 3-axis formation flying algorithm in an autonomous executive flight code onboard the New Millennium Program's (NMP) Earth Observing-1 (EO-1) spacecraft. This paper describes the mathematical background of the autonomous formation flying algorithm and the onboard flight design and presents the validation results of this unique system. Results from functionality assessment through fully autonomous maneuver control are presented as comparisons between the onboard EO-1 operational autonomous control system called AutoCon(tm), its ground-based predecessor, and a standalone algorithm.

Folta, David C.↗