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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A survey of intelligent tutoring systems: Implications for complex dynamic systems

An overview of the research in the field of intelligent tutorial systems (ITS) is provided. The various approaches in the design and implementation of ITS are examined and discussed in the context of problem solving in an environment of a complex dynamic system (CDS). Issues pertaining to a CDS and the nature of human problem solving especially in light of a CDS are considered. An overview of the architecture of an ITS is provided as the basis for the in-depth examination of various systems. Finally, the implications for the design and evaluation of an ITS are discussed.

Chu, Rose W.↗
NASA NTRS1987

A qualitative model of human interaction with complex dynamic systems

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Hess, Ronald A.↗
NASA NTRS1987

A Qualitative Model of Human Interaction with Complex Dynamic Systems

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Hess, Ronald A.↗
NASA NTRS1990

An ICAI architecture for troubleshooting in complex, dynamic systems

Ahab, an intelligent computer-aided instruction (ICAI) program, illustrates an architecture for simulator-based ICAI programs to teach troubleshooting in complex, dynamic environments. The architecture posits three elements of a computerized instructor: the task model, the student model, and the instructional module. The task model is a prescriptive model of expert performance that uses symptomatic and topographic search strategies to provide students with directed problem-solving aids. The student model is a descriptive model of student performance in the context of the task model. This student model compares the student and task models, critiques student performance, and provides interactive performance feedback. The instructional module coordinates information presented by the instructional media, the task model, and the student model so that each student receives individualized instruction. Concept and metaconcept knowledge that supports these elements is contained in frames and production rules, respectively. The results of an experimental evaluation are discussed. They support the hypothesis that training with an adaptive online system built using the Ahab architecture produces better performance than training using simulator practice alone, at least with unfamiliar problems. It is not sufficient to develop an expert strategy and present it to students using offline materials. The training is most effective if it adapts to individual student needs.

Fath, Janet L.↗
NASA NTRS1993

Cognitive engineering models: A prerequisite to the design of human-computer interaction in complex dynamic systems

This chapter examines a class of human-computer interaction applications, specifically the design of human-computer interaction for the operators of complex systems. Such systems include space systems (e.g., manned systems such as the Shuttle or space station, and unmanned systems such as NASA scientific satellites), aviation systems (e.g., the flight deck of 'glass cockpit' airplanes or air traffic control) and industrial systems (e.g., power plants, telephone networks, and sophisticated, e.g., 'lights out,' manufacturing facilities). The main body of human-computer interaction (HCI) research complements but does not directly address the primary issues involved in human-computer interaction design for operators of complex systems. Interfaces to complex systems are somewhat special. The 'user' in such systems - i.e., the human operator responsible for safe and effective system operation - is highly skilled, someone who in human-machine systems engineering is sometimes characterized as 'well trained, well motivated'. The 'job' or task context is paramount and, thus, human-computer interaction is subordinate to human job interaction. The design of human interaction with complex systems, i.e., the design of human job interaction, is sometimes called cognitive engineering.

Mitchell, Christine M.↗
NASA NTRS1993

Control of Complex Dynamic Systems by Neural Networks

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Spall, James C.↗
NASA NTRS1995

Robust integrated neurocontroller for complex dynamic systems

The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system; (2) Development of a robust neurocontroller system, and; (3) Integration of above systems to create a robust Integration Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.

Zein-Sabbato, S.↗
NASA NTRS1996

Robust Integrated Neurocontroller for Complex Dynamic Systems

The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are: adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as: sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system, (2) Development of a robust neurocontroller system, and 3. Integration of above systems to create a Robust Integrated Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.

Zein-Sabatto, S.↗
NASA NTRS2001

Toward Failure Modeling In Complex Dynamic Systems: Impact of Design and Manufacturing Variations

When designing vehicle vibration monitoring systems for aerospace devices, it is common to use well-established models of vibration features to determine whether failures or defects exist. Most of the algorithms used for failure detection rely on these models to detect significant changes during a flight environment. In actual practice, however, most vehicle vibration monitoring systems are corrupted by high rates of false alarms and missed detections. Research conducted at the NASA Ames Research Center has determined that a major reason for the high rates of false alarms and missed detections is the numerous sources of statistical variations that are not taken into account in the. modeling assumptions. In this paper, we address one such source of variations, namely, those caused during the design and manufacturing of rotating machinery components that make up aerospace systems. We present a novel way of modeling the vibration response by including design variations via probabilistic methods. The results demonstrate initial feasibility of the method, showing great promise in developing a general methodology for designing more accurate aerospace vehicle vibration monitoring systems.

Tumer, Irem Y.↗
NASA NTRS1994

Hidden Markov Models for Fault Detection in Dynamic Systems

Continuous monitoring of complex dynamic systems is an increasingly important issue in diverse areas such as nuclear plant safety, production line reliability, and medical health monitoring systems. Recent advances in both sensor technology and computational capabilities have made on-line permanent monitoring much more feasible than it was in the past. In this paper it is shown that a pattern recognition system combined with a finite-state hidden Markov model provides a particularly useful method for modelling temporal context in continuous monitoring. The parameters of the Markov model are derived from gross failure statistics such as the mean time between failures. The model is validated on a real-world fault diagnosis problem and it is shown that Markov modelling in this context offers significant practical benefits.

Smyth, Padhraic↗
NASA NTRS1989

A NASTRAN/TREETOPS solution to a flexible, multi-body dynamics and controls problem on a UNIX workstation

Demands for nonlinear time history simulations of large, flexible multibody dynamic systems has created a need for efficient interfaces between finite-element modeling programs and time-history simulations. One such interface, TREEFLX, an interface between NASTRAN and TREETOPS, a nonlinear dynamics and controls time history simulation for multibody structures, is presented and demonstrated via example using the proposed Space Station Mobile Remote Manipulator System (MRMS). The ability to run all three programs (NASTRAN, TREEFLX and TREETOPS), in addition to other programs used for controller design and model reduction (such as DMATLAB and TREESEL, both described), under a UNIX Workstation environment demonstrates the flexibility engineers now have in designing, developing and testing control systems for dynamically complex systems.

Benavente, Javier E.↗
NASA NTRS1992

Increasingly automated procedure acquisition in dynamic systems

Procedures are widely used by operators for controlling complex dynamic systems. Currently, most development of such procedures is done manually, consuming a large amount of paper, time, and manpower in the process. While automated knowledge acquisition is an active field of research, not much attention has been paid to the problem of computer-assisted acquisition and refinement of complex procedures for dynamic systems. The Procedure Acquisition for Reactive Control Assistant (PARC), which is designed to assist users in more systematically and automatically encoding and refining complex procedures. PARC is able to elicit knowledge interactively from the user during operation of the dynamic system. We categorize procedure refinement into two stages: diagnosis - diagnose the failure and choose a repair - and repair - plan and perform the repair. The basic approach taken in PARC is to assist the user in all steps of this process by providing increased levels of assistance with layered tools. We illustrate the operation of PARC in refining procedures for the control of a robot arm.

Mathe, Nathalie↗
NASA NTRS1989

Characteristics of an ITS that evolves from tutor to operator's assistant

This paper discusses the motivation and goals of a research project which addresses the problems and issues of operator training in complex engineering sytems. The research proposes a tutor/aid paradigm for the design of an intelligent tutoring system (ITS) that evolves from a tutor to an operator's assistant for supervisory control of complex dynamic systems. Characteristics of an intelligent tutoring/aiding system are identified with respect to the representation of domain knowledge, the tutor's pedagogical structure, and the student knowledge representation. The research represents a first step in the design of an intelligent complex dynamic systems.

Chu, R. W.↗
NASA NTRS1988

OFMspert - Inference of operator intentions in supervisory control using a blackboard architecture

The authors proposes an architecture for an expert system that can function as an operator's associate in the supervisory control of a complex dynamic system. Called OFMspert (operator function model (OFM) expert system), the architecture uses the operator function modeling methodology as the basis for the design. The authors put emphasis on the understanding capabilities, i.e., the intent referencing property, of an operator's associate. The authors define the generic structure of OFMspert, particularly those features that support intent inferencing. They also describe the implementation and validation of OFMspert in GT-MSOCC (Georgia Tech-Multisatellite Operations Control Center), a laboratory domain designed to support research in human-computer interaction and decision aiding in complex, dynamic systems.

Jones, Patricia S.↗
NASA NTRS1999

GT-CATS: Tracking Operator Activities in Complex Systems

Human operators of complex dynamic systems can experience difficulties supervising advanced control automation. One remedy is to develop intelligent aiding systems that can provide operators with context-sensitive advice and reminders. The research reported herein proposes, implements, and evaluates a methodology for activity tracking, a form of intent inferencing that can supply the knowledge required for an intelligent aid by constructing and maintaining a representation of operator activities in real time. The methodology was implemented in the Georgia Tech Crew Activity Tracking System (GT-CATS), which predicts and interprets the actions performed by Boeing 757/767 pilots navigating using autopilot flight modes. This report first describes research on intent inferencing and complex modes of automation. It then provides a detailed description of the GT-CATS methodology, knowledge structures, and processing scheme. The results of an experimental evaluation using airline pilots are given. The results show that GT-CATS was effective in predicting and interpreting pilot actions in real time.

Callantine, Todd J.↗