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NASA NTRS2014

Re-Engineering the ISS Payload Operations Control Center During Increased Utilization and Critical Onboard Events

With an increase in utilization and hours of payload operations being executed onboard the International Space Station (ISS), upgrading the NASA Marshall Space Flight Center (MSFC) Huntsville Operations Support Center (HOSC) ISS Payload Control Area (PCA) was essential to gaining efficiencies and assurance of current and future payload health and science return. PCA houses the Payload Operations Integration Center (POIC) responsible for the execution of all NASA payloads onboard the ISS. POIC Flight Controllers are responsible for the operation of voice, stowage, command, telemetry, video, power, thermal, and environmental control in support of ISS science experiments. The methodologies and execution of the PCA refurbishment were planned and performed within a four-month period in order to assure uninterrupted operation of ISS payloads and minimal impacts to payload operations teams. To vacate the PCA, three additional HOSC control rooms were reconfigured to handle ISS real-time operations, Backup Control Center (BCC) to Mission Control in Houston, simulations, and testing functions. This involved coordination and cooperation from teams of ISS operations controllers, multiple engineering and design disciplines, management, and construction companies performing an array of activities simultaneously and in sync delivering a final product with no issues that impacted the schedule. For each console operator discipline, studies of Information Technology (IT) tools and equipment layouts, ergonomics, and lines of sight were performed. Infusing some of the latest IT into the project was an essential goal in ensuring future growth and success of the ISS payload science returns. Engineering evaluations led to a state of the art Video Wall implementation and more efficient ethernet cabling distribution providing the latest products and the best solution for the POIC. These engineering innovations led to cost savings for the project. Constraints involved in the management of the project included executing over 450 crew-hours of ISS real-time payload operations including a major onboard communications upgrade, SpaceX un-berth, a Soyuz launch, roll-out of ISS live video and interviews from the POIC, annual BCC certification and hurricane season, and ISS simulations and testing. Continuous ISS payload operations were possible during the PCA facility modifications with the reconfiguration of four control rooms and standup of two temporary control areas. Another major restriction to the project was an ongoing facility upgrade that included a NASA Headquarters mandated replacement of all electrical and mechanical systems and replacement of an external generator. These upgrades required a facility power outage during the PCA upgrades. The project also encompassed console layout designs and ordering, amenities selections and ordering, excessing of old equipment, moves, disposal of old IT equipment, camera installations, facility tour re-schedules, and contract justifications. These were just some of the tasks needed for a successful project. This paper describes the logistics and lessons learned in upgrading a control center capability in the middle of complex real-time operations. Combining the efficiencies of controller interaction and new technology infusion were prime drivers for this upgrade to handle the increased utilization of science research on ISS. The success of this project could not jeopardize the current operations while these facility upgrades occurred.

Dudley, Stephanie R. B.↗
NASA NTRS1994

POINTER: Portable Intelligent Trainer for External Robotics

Intelligent tutoring systems (ITS's) play an increasing role in training and education of people with different levels of skill and knowledge. As compared to conventional computer based training (CBT) an ITS provides more tailored instruction by trying to mimic the teaching behavior of a human instructor as much as possible and is therefore much more flexible. This paper starts with an introduction to ITS's, followed by the description of an ITS for training of an (astronaut) operator in monitoring and controlling robotic arm procedures. The robotic arm will be used for exchange of equipment between a space station and a space plane involving critical and accurate movements of the robotic arm. The ITS for this application, called Pointer, is developed by TNO Physics and Electronics Laboratory and is based upon an existing ITS that includes procedural training. Pointer has been developed on a workstation whereas the target platform was a portable computer. Therefore, a lot of attention had to be paid to scaling effects and keeping up with user friendliness of the much smaller user interface. Although the learning domain was the control of a robotic arm, it is clear that use of intelligent training technologies on a portable computer has many other applications (payload operations, operation control rooms, etc.). Training can occur at any time and place in an attractive and cost effective way.

Kuiper, Hilbert↗
NASA NTRS2006

Assessment of Postflight Locomotor Performance Utilizing a Test of Functional Mobility: Strategic and Adaptive Responses

Space flight induces adaptive modification in sensorimotor function, allowing crewmembers to operate in the unique microgravity environment. This adaptive state, however, is inappropriate for a terrestrial environment. During a re-adaptation period upon their return to Earth, crewmembers experience alterations in sensorimotor function, causing various disturbances in perception, spatial orientation, posture, gait, and eye-head coordination. Following long duration space flight, sensorimotor dysfunction would prevent or extend the time required to make an emergency egress from the vehicle; compromising crew safety and mission objectives. We are investigating two types of motor learning that may interact with each other and influence a crewmember's ability to re-adapt to Earth's gravity environment. In strategic learning, crewmembers make rapid modifications in their motor control strategy emphasizing error reduction. This type of learning may be critical during the first minutes and hours after landing. In adaptive learning, long-term plastic transformations occur, involving morphological changes and synaptic modification. In recent literature these two behavioral components have been associated with separate brain structures that control the execution of motor strategies: the strategic component was linked to the posterior parietal cortex and the adaptive component was linked to the cerebellum (Pisella, et al. 2004). The goal of this paper was to demonstrate the relative contributions of the strategic and adaptive components to the re-adaptation process in locomotor control after long duration space flight missions on the International Space Station (ISS). The Functional Mobility Test (FMT) was developed to assess crewmember s ability to ambulate postflight from an operational and functional perspective. Sixteen crewmembers were tested preflight (3 sessions) and postflight (days 1, 2, 4, 7, 25) following a long duration space flight (approx 6 months) on the ISS. We have further analyzed the FMT data to characterize strategic and adaptive components during the postflight readaptation period. Crewmembers walked at a preferred pace through an obstacle course set up on a base of 10 cm thick medium density foam (Sunmate Foam, Dynamic Systems, Inc., Leicester, NC). The 6.0m X 4.0m course consisted of several pylons made of foam; a Styrofoam barrier 46.0cm high that crewmembers stepped over; and a portal constructed of two Styrofoam blocks, each 31cm high, with a horizontal bar covered by foam and suspended from the ceiling which was adjusted to the height of the crewmember s shoulder. The portal required crewmembers to bend at the waist and step over a barrier simultaneously. All obstacles were lightweight, soft and easily knocked over. Crewmembers were instructed to walk through the course as quickly and as safely as possible without touching any of the objects on the course. This task was performed three times in the clockwise direction and three times in the counterclockwise direction that was randomly chosen. The dependent measures for each trial were: time to complete the course (seconds) and the number of obstacles touched or knocked down. For each crewmember, the time to complete each FMT trial from postflight days 1, 2, 4, 7 and 25 were further analyzed. A single logarithmic curve using a least squares calculation was fit through these data to produce a single comprehensive curve (macro). This macro curve composed of data spanning 25 days, illustrates the re-adaptive learning function over the longer time scale term. Additionally, logarithmic curves were fit to the 6 data trials within each individual post flight test day to produce 5 separate daily curves. These micro curves, produced from data obtained over the course of minutes, illustrates the strategic learning function exhibited over a relative shorter time scale. The macro curve for all subjects exhibited adaptive motor learning patterns over the 25 day period. Howev, 9/16 crewmembers exhibited significant strategic motor learning patterns in their micro curves, as defined by m > 1 in the equation of the line y=m*LN(x) +b. These data indicate that postflight recovery in locomotor function involves both strategic and adaptive mechanisms. Future countermeasures will be designed to enhance both recovery processes.

Warren, L. E.↗
NASA NTRS1990

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Karr, C. L.↗
NASA NTRS1988

Autonomous control systems - Architecture and fundamental issues

A hierarchical functional autonomous controller architecture is introduced. In particular, the architecture for the control of future space vehicles is described in detail; it is designed to ensure the autonomous operation of the control system and it allows interaction with the pilot and crew/ground station, and the systems on board the autonomous vehicle. The fundamental issues in autonomous control system modeling and analysis are discussed. It is proposed to utilize a hybrid approach to modeling and analysis of autonomous systems. This will incorporate conventional control methods based on differential equations and techniques for the analysis of systems described with a symbolic formalism. In this way, the theory of conventional control can be fully utilized. It is stressed that autonomy is the design requirement and intelligent control methods appear at present, to offer some of the necessary tools to achieve autonomy. A conventional approach may evolve and replace some or all of the `intelligent' functions. It is shown that in addition to conventional controllers, the autonomous control system incorporates planning, learning, and FDI (fault detection and identification).

Antsaklis, P. J.↗
NASA NTRS1994

Fiber Optic Control System integration for advanced aircraft. Electro-optic and sensor fabrication, integration, and environmental testing for flight control systems

This report describes the design, development, and testing of passive fiber optic sensors and a multiplexing electro-optic architecture (EOA) for installation and flight test on a NASA-owned F-18 aircraft. This hardware was developed under the Fiber Optic Control Systems for Advanced Aircraft program, part of a multiyear NASA initiative to design, develop, and demonstrate through flight test 'fly-by-light' systems for application to advanced aircraft flight and propulsion control. This development included the design and production of 10 passive optical sensors and associated multiplexed EOA hardware based on wavelength division multiplexed (WDM) technology. A variety of sensor types (rotary position, linear position, temperature, and pressure) incorporating a broad range of sensor technologies (WDM analog, WDM digital, analog microbend, and fluorescent time rate of decay) were obtained from different manufacturers and functionally integrated with an independently designed EOA. The sensors were built for installation in a variety of aircraft locations, placing the sensors in a variety of harsh environments. The sensors and EOA were designed and built to have the resulting devices be as close as practical to a production system. The integrated system was delivered to NASA for flight testing on a NASA-owned F-18 aircraft. Development and integration testing of the system provided valuable information as to which sensor types were simplest to design and build for a military aircraft environment and which types were simplest to operate with a multiplexed EOA. Not all sensor types met the full range of performance and environmental requirements. EOA development problems provided information on directions to pursue in future fly-by-light flight control development programs. Lessons learned in the development of the EOA and sensor hardware are summarized.

Seal, Daniel W.↗
NASA NTRS2000

Intelligent Tutoring Systems for Procedural Task Training of Remote Payload Operations at NASA

Intelligent Tutoring Systems (ITSs) encode and apply the subject matter and teaching expertise of experienced instructors to provide students with individualized instruction automatically. ITSs complement training simulators by providing automated instruction when it is not economical or feasible to dedicate an instructor to each student during training simulations. Despite their proven training effectiveness and favorable operating cost, however, relatively few ITSs are in use. This is largely because it is usually costly and difficult to encode the task knowledge used by the ITS to evaluate the student's actions and assess the student's performance. Procedural tasks are tasks for which there exist procedures, guidelines, and strategies that determine the correct set of steps to be taken within each situation. To lower the cost and difficulty of creating tutoring systems for procedural task training, Stottler Henke Associates, Inc. (SHAI) worked closely with the Operations Training Group at NASA's Marshall Space Flight Center to develop the Task Tutor Toolkit (T (exp 3)), a generic tutoring system shell and scenario authoring tool. The Task Tutor Toolkit employs a case-based reasoning approach where the instructor creates a procedure template that specifies the range of student actions that are "correct" within each scenario. Because each procedure template is specific to a single scenario, the system can employ relatively simple reasoning methods to represent a correct set of actions and assess student performance. This simplicity enables a non-programmer to specify task knowledge quickly and easily by via graphical user interface, using a "demonstrate, generalize, and annotate" paradigm, that recognizes the range of possible valid actions and infers principles understood (or misunderstood) by the student when those actions are carried out. The Task Tutor Toolkit was also designed to be modular and general, so that it can be interfaced with a wide range of training simulators and support a variety of training domains. SHAI and NASA applied the Task Tutor Toolkit to create the Remote Payload Operations Tutor (RPOT). RPOT is a specific tutoring system application which lets scientists who are new to space mission operations learn to monitor and control their experiments aboard the International Space Station according to NASA payload regulations, guidelines, and procedures. The RPOT simulator lets students practice these skills by monitoring the telemetry variable values of a simple, hypothetical experiment, sending commands to the experiment, coordinating with NASA personnel via voice communication loops, and submitting and retrieving information via documents and forms. At the end of each scenario, RPOT displays the principles correctly or incorrectly demonstrated by the student, along with explanations and background information. The effectiveness of RPOT and the Task Tutor Toolkit are currently under evaluation at NASA.

Ong, James↗
NASA NTRS2010

Dynamically Evolving Sectors for Convective Weather Impact

A new strategy for altering existing sector boundaries in response to blocking convective weather is presented. This method seeks to improve the reduced capacity of sectors directly affected by weather by moving boundaries in a direction that offers the greatest capacity improvement. The boundary deformations are shared by neighboring sectors within the region in a manner that preserves their shapes and sizes as much as possible. This reduces the controller workload involved with learning new sector designs. The algorithm that produces the altered sectors is based on a force-deflection mesh model that needs only nominal traffic patterns and the shape of the blocking weather for input. It does not require weather-affected traffic patterns that would have to be predicted by simulation. When compared to an existing optimal sector design method, the sectors produced by the new algorithm are more similar to the original sector shapes, resulting in sectors that may be more suitable for operational use because the change is not as drastic. Also, preliminary results show that this method produces sectors that can equitably distribute the workload of rerouted weather-affected traffic throughout the region where inclement weather is present. This is demonstrated by sector aircraft count distributions of simulated traffic in weather-affected regions.

Drew, Michael C.↗
NASA NTRS2000

X-38 Vehicle 131R Free Flights 1 and 2

The X-38 program is using a modern flight control system (FCS) architecture originally developed by Honeywell called MACH. During last year's SAE G&C subcommittee meeting, we outlined the design, implementation and testing of MACH in X-38 Vehicles 132, 131R & 201. During this year's SAE meeting, I'll focus upon the first two free flights of V131R, describing what caused the roll-over in FF1 and how we fixed it for FF2. I only have 30 minutes, so it will be a quick summary including VHS video. X-38 is a NASA JSC/DFRC experimental flight test program developing a series of prototypes for an International Space Station (ISS) Crew Return Vehicle (CRV), often described as an ISS "lifeboat." X-38 Vehicle 132 Free Flight 3 was the first flight test of a modern FCS architecture called Multi-Application ControlH (MACH), developed by the Honeywell Technology Center in Minneapolis and Honeywell's Houston Engineering Center. MACH wraps classical Proportional+integral (P+I) outer attitude loops around modern dynamic inversion attitude rate loops. The presentation at last year's SAE Aerospace Meeting No. 85 focused upon the design and testing of the FCS algorithm and Vehicle 132 Free Flight 3. This presentation will summarize flight control and aerodynamics lessons learned during Free Flights 1 and 2 of Vehicle 131R, a subsonic test vehicle laying the groundwork for the orbital/entry test of Vehicle 201 in 2003.

Munday, Steve↗
NASA NTRS2016

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

Davila, Carlos G.↗
NASA NTRS2018

Learn to Fly Test Setup and Concept of Operations

The NASA Learn-to-Fly (L2F) project recently completed a series of flight demonstrations of its learning algorithm for flight control at Fort A. P. Hill in Virginia. This paper discusses the test setup and concept of operations (ConOps) used by the L2F team. Unmanned airframe demonstrators for testing the research algorithms included a modified commercial off-the-shelf subscale powered airplane, plus four gliders – two of which had an unconventional configuration and were fabricated using a “rapid” prototyping technique. Avionics system similarities and differences between the test aircraft are described, as well as ground testing in preparation for flight. The ConOps discussion includes the development of a tethered helium balloon drop launch technique for the glider demonstrators. This launch method was chosen for its potential to be inexpensive and allow for rapid turn-around for multiple glider launches – but it also presented challenges, such as balloon tether avoidance, high angle of attack, low dynamic pressure initial conditions, and susceptibility to winds. A remotely piloted approach employing high-end hobbyist radio controlled (R/C) hardware was used for the powered demonstrator. This approach accommodated the interaction between the R/C flight system and the research flight control computer, engaging the L2F algorithm at varying initial conditions and artificially reducing the aircraft stability to stress the algorithm.

Riddick, Stephen E↗
NASA NTRS1993

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results

As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.

Jani, Yashvant↗
NASA NTRS2009

Evaluation of Learning Associated with Multiple Exposures to Computerized Dynamic Posturography

Computerized dynamic posturography has been used to quantitatively assess the time course of functional sensorimotor recovery after exposure to spaceflight or to groundbased analogs such as head-down bed rest. An assessment of balance recovery may be confounded as subjects develop new strategies through repeated exposures to test paradigms. The purpose of this control study was to characterize the learning effects of sensory organization and motor control tests across multiple sessions. METHODS: Twenty-eight healthy subjects were tested over four sessions. To examine the effects of between-session interval, subjects were assigned to one of four groups in which the interval between the 1 st and 2nd sessions was 7 (+/- 1) days, 14 (+/-1) days, 28 (+/-2) days, or 56 (+/-3) days. The interval between remaining sessions was 28 (+/-4) days. Peak-to-peak anterior-posterior sway was measured during standard Sensory Organization Tests (SOTs) using either fixed or unstable sway-referenced support with eyes open, eyes closed, or sway-referenced vision. Sway was also measured during modified SOTs using eyes-closed conditions with either static or dynamic head tilts. Postural recovery to unexpected support surface perturbations (translations or rotations) was measured during Motor Control Tests. The test order was block randomized across subjects. RESULTS: The learning effects varied with test condition. There were no measurable differences with a stable support surface. The more challenging conditions (unstable support surface with and without head tilts) led to greater differences and took more trials to stabilize. The effect of time interval between the first two sessions was negligible across conditions. Evidence suggested that learning carried across similar conditions (such as unstable support SOTs). DISCUSSION: Familiarization session and/or trials are recommended to minimize learning effects when characterizing functional recovery after exposure to altered sensory environments. The number of practice trials required depends on task difficulty and similarity across conditions. Learning statement: This presentation will review the learning effects of computerized d

Dean, S. Lance↗
NASA NTRS1988

Expert systems for adaptive control of large space structures

It is expected that space systems for the future will evolve to structures of unprecedented size with associated extreme control requirements. A method is necessary that is sufficiently general to initiate stable control of a vehicle and subsequently learn the true nature of the structure. It is suggested that a suitable constructed expert system (ES) would be capable of learning by appending observations to a knowledge base. To verify that an ES can control a large space structure, numerical simulations of a simple structure subjected to periodic vibrations and the performance of a classical controllers were performed. The ES was then exercised to show its ability to truthfully mimic nominal control and to demonstrate its superiority to the classical controller, given sensor failures. An ES generating software package, The Intelligent Machine Model, was employed. It uses the pattern matching technique. Results of this program are discussed.

Gartrell, Charles F.↗
NASA NTRS2002

Managing Risk in Safety Critical Operations - Lessons Learned from Space Operations

The Mission Control Center (MCC) at Johnson Space Center (JSC) has a rich legacy of supporting Human Space Flight operations throughout the Apollo, Shuttle and International Space Station eras. Through the evolution of ground operations and the Mission Control Center facility, NASA has gained a wealth of experience of what it takes to manage the risk in Safety Critical Operations, especially when human life is at risk. The focus of the presentation will be on the processes (training, operational rigor, team dynamics) that enable the JSC/MCC team to be so successful. The presentation will also share the evolution of the Mission Control Center architecture and how the evolution was introduced while managing the risk to the programs supported by the team. The details of the MCC architecture (e.g., the specific software, hardware or tools used in the facility) will not be shared at the conference since it would not give any additional insight as to how risk is managed in Space Operations.

Gonzalez, Steven A.↗