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Results for “Fuzzy Logic System”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Smart monitoring system based on adaptive current control for superconducting cable test

A smart monitoring system for superconducting cable test is proposed with an adaptive current control of a superconducting transformer secondary. The design, based on Fuzzy Gain Scheduling, allows the controller parameters to adapt continuously, and finely, to the working variations arising from transformer nonlinear dynamics. The control system is integrated in a fully digital control loop, with all the related benefits, i.e., high noise rejection, ease of implementation/modification, and so on. In particular, an accurate model of the system, controlled by a Fuzzy Gain Scheduler of the superconducting transformer, was achieved by an experimental campaign through the working domain at several current ramp rates. The model performance was characterized by simulation, under all the main operating conditions, in order to guide the controller design. Finally, the proposed monitoring system was experimentally validated at European Organization for Nuclear Research (CERN) in comparison to the state-of-the-art control system [P. Arpaia, L. Bottura, G. Montenero, and S. Le Naour, “Performance improvement of a measurement station for superconducting cable test,” Rev. Sci. Instrum.83, 095111 (2012)] of the Facility for the Research on Superconducting Cables, achieving a significant performance improvement: a reduction in the system overshoot by 50%, with a related attenuation of the corresponding dynamic residual error (both absolute and RMS) up to 52%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1992

Advanced training systems for the next decade and beyond

Autonomous training systems grouped under the term intelligent computer-aided training (ICAT) are described with references to their intelligent technologies. An ICAT system was developed for the deployment of the payload-assist module satellites from the Space Shuttle, fault detection in vacuum-vent lines on Spacelab, main-propulsion pneumatics, and for instrument pointing systems. The basic ICAT architecture is delineated with descriptions of elements such as the user interface, domain expert, traning-session manager, trainee model, and training-scenario generator. The ICAT systems employ advanced technologies including knowledge acquisition, virtual environments, and fuzzy logic. The general architecture of the ICAT systems is shown to be fully developed and applicable to the construction of seven different tasks, and the ICAT systems are concluded to enhance the training of aerospace personnel.

Loftin, R. B.↗
DOE OSTI2016

Development of a Severe Accident Analysis Engine Using Approximate Reasoning

In hypothetical severe accident scenarios, nuclear power plant safety is challenged by core degradation and release of fission products. To aid the plant operator and technical support center staff, research has been performed to develop understanding of severe accident phenomenology, assessing their risk and devise measures to mitigate accident consequences (Sehgal, 2011). Nuclear power plant accidents involve highly complex physical and chemical interactions, a large number of safety related systems, and emergency operating procedures (EOP) and severe accident management guidelines (SAMG). Large number of factors with uncertainty and stress can overwhelm the operators. The overall objective of the present research is to develop a methodology and tools that an operator can use to predict severe accident progression and make effective decisions to arrest and manage the accident under large uncertainties. Towards this goal, this study examines a novel approach that brings to bear a combined use of techniques in artificial intelligence (Russel and Norvig, 2010), including Bayesian Network (BN), Answer Set Programming (ASP) and Fuzzy Inference (FI) to achieve faster-than-real-time, yet sufficiently accurate analysis of severe accident progression. Bayesian network (BN) is a type of probabilistic graphical models that can be used to represent knowledge and conditional dependencies about an uncertain realm. It is useful for capturing the causal relationships in a complex system. It also allows prediction via Bayesian inference (Kelly and Smith, 2012). It is composed of nodes and edges, which express random variables and probabilistic relationships between the nodes, respectively. This relationship, given by conditional probability is calculated using stochastic methods. The BN is used in several systems for analysis and management of accident risk; e.g. (Knochenhauer and Frid, 2010). In those systems, the nodes or so-called 'effective factors' are presumably known within the network by system descriptions in SAMGs. There are two steps to configure the BN, namely (i) identifying compositions within the network and (ii) defining conditional dependency between nodes. The first step is performed following the spirit of ASP (Baral, 2003) using Prolog (Wielemaker, 2005). Knowledge representation is used to build databases required for processing by ASP. Knowledge representation is defined as the expression of knowledge in a way that is understandable to both human and computers. Originally knowledge indicates data, information and facts. To focus, knowledge should be formed to achieve a goal. In a severe accident, key physics and safety related components status are defined according to a failure mode. The conditional dependencies between nodes are generated by approximate reasoning using Fuzzy inference (Zadeh, 1995). Fuzzy logic is a multiple-valued logic to treat ambiguous features such as linguistic description. It indicates possibility, which is distinguished from probability. Fuzzy set consists of linguistic variables according to the fixed rule. In other words, even uncertain states could be expressed as not binary logic, but as many-value logic by rule-based technology. Fuzzy control system is comprised of Fuzzy inference engine that is performed based on the 'If-Then Rule'. The 'If-Then Rule' is of the following form. If Y1 is B11 and Y2 is B12 then Z is C1. If Y1 is B21 and Y2 is B22 then Z is C2. Y1 and Y2 are inputs, which indicate conditional variables. Z indicates decision-making and both B and C are linguistic variables. With this rule-based technique, the approximate reasoning can be realized by Fuzzy inference. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1992

Flexible body control using neural networks

Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

Mccullough, Claire L.↗
NASA NTRS1992

Learning and tuning fuzzy logic controllers through reinforcements

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗
NASA NTRS1998

Approximation Of Multi-Valued Inverse Functions Using Clustering And Sugeno Fuzzy Inference

Finding the inverse of a continuous function can be challenging and computationally expensive when the inverse function is multi-valued. Difficulties may be compounded when the function itself is difficult to evaluate. We show that we can use fuzzy-logic approximators such as Sugeno inference systems to compute the inverse on-line. To do so, a fuzzy clustering algorithm can be used in conjunction with a discriminating function to split the function data into branches for the different values of the forward function. These data sets are then fed into a recursive least-squares learning algorithm that finds the proper coefficients of the Sugeno approximators; each Sugeno approximator finds one value of the inverse function. Discussions about the accuracy of the approximation will be included.

Walden, Maria A.↗
NASA NTRS1992

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗
NASA NTRS1991

A neural network architecture for implementation of expert systems for real time monitoring

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

Ramamoorthy, P. A.↗
NASA NTRS1994

Automated maneuver planning using a fuzzy logic algorithm

Spacecraft orbital control requires intensive interaction between the analyst and the system used to model the spacecraft trajectory. For orbits with right mission constraints and a large number of maneuvers, this interaction is difficult or expensive to accomplish in a timely manner. Some automation of maneuver planning can reduce these difficulties for maneuver-intensive missions. One approach to this automation is to use fuzzy logic in the control mechanism. Such a prototype system currently under development is discussed. The Tropical Rainfall Measurement Mission (TRMM) is one of several missions that could benefit from automated maneuver planning. TRMM is scheduled for launch in August 1997. The spacecraft is to be maintained in a 350-km circular orbit throughout the 3-year lifetime of the mission, with very small variations in this orbit allowed. Since solar maximum will occur as early as 1999, the solar activity during the TRMM mission will be increasing. The increasing solar activity will result in orbital maneuvers being performed as often as every other day. The results of automated maneuver planning for the TRMM mission will be presented to demonstrate the prototype of the fuzzy logic tool.

Conway, D.↗
NASA NTRS1989

Applications of fuzzy sets to rule-based expert system development

Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules indecision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.

Lea, Robert N.↗
NASA NTRS1989

Applications of fuzzy sets to rule-based expert system development

Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.

Lea, Robert N.↗
NASA NTRS1995

Advanced control design for hybrid turboelectric vehicle

The new environment standards are a challenge and opportunity for industry and government who manufacture and operate urban mass transient vehicles. A research investigation to provide control scheme for efficient power management of the vehicle is in progress. Different design requirements using functional analysis and trade studies of alternate power sources and controls have been performed. The design issues include portability, weight and emission/fuel efficiency of induction motor, permanent magnet and battery. A strategic design scheme to manage power requirements using advanced control systems is presented. It exploits fuzzy logic, technology and rule based decision support scheme. The benefits of our study will enhance the economic and technical feasibility of technological needs to provide low emission/fuel efficient urban mass transit bus. The design team includes undergraduate researchers in our department. Sample results using NASA HTEV simulation tool are presented.

Abban, Joseph↗
NASA NTRS1991

Intelligent systems technology infrastructure for integrated systems

A system infrastructure must be properly designed and integrated from the conceptual development phase to accommodate evolutionary intelligent technologies. Several technology development activities were identified that may have application to rendezvous and capture systems. Optical correlators in conjunction with fuzzy logic control might be used for the identification, tracking, and capture of either cooperative or non-cooperative targets without the intensive computational requirements associated with vision processing. A hybrid digital/analog system was developed and tested with a robotic arm. An aircraft refueling application demonstration is planned within two years. Initially this demonstration will be ground based with a follow-on air based demonstration. System dependability measurement and modeling techniques are being developed for fault management applications. This involves usage of incremental solution/evaluation techniques and modularized systems to facilitate reuse and to take advantage of natural partitions in system models. Though not yet commercially available and currently subject to accuracy limitations, technology is being developed to perform optical matrix operations to enhance computational speed. Optical terrain recognition using camera image sequencing processed with optical correlators is being developed to determine position and velocity in support of lander guidance. The system is planned for testing in conjunction with Dryden Flight Research Facility. Advanced architecture technology is defining open architecture design constraints, test bed concepts (processors, multiple hardware/software and multi-dimensional user support, knowledge/tool sharing infrastructure), and software engineering interface issues.

Lum, Henry↗
NASA NTRS1996

Design of Power System Architectures for Small Spacecraft Systems

The objective of this research is to perform a trade study on several candidate power system architectures for small spacecrafts to be used in NASA's new millennium program. Three initial candidate architectures have been proposed by NASA and two other candidate architectures have been proposed by Howard University. Howard University is currently conducting the necessary analysis, synthesis, and simulation needed to perform the trade studies and arrive at the optimal power system architecture. Statistical, sensitivity and tolerant studies has been performed on the systems. It is concluded from present studies that certain components such as the series regulators, buck-boost converters and power converters can be minimized while retaining the desired functionality of the overall architecture. This in conjunction with battery scalability studies and system efficiency studies have enabled us to develop more economic architectures. Future studies will include artificial neural networks and fuzzy logic to analyze the performance of the systems. Fault simulation studies and fault diagnosis studies using EMTP and artificial neural networks will also be conducted.

Momoh, James A.↗
NASA NTRS2001

Monitoring the Microgravity Environment Quality On-Board the International Space Station Using Soft Computing Techniques

This paper presents an artificial intelligence monitoring system developed by the NASA Glenn Principal Investigator Microgravity Services project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment in time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the principal investigator teams can monitor via a graphical display, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, platform structural modes, etc., and decide whether or not to run their experiments based on the acceleration environment associated with a specific event. This monitoring system is focused primarily on detecting the vibratory disturbance sources, but could be used as well to detect some of the transient disturbance sources, depending on the events duration. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗
NASA NTRS2002

Monitoring the Microgravity Environment Quality On-board the International Space Station Using Soft Computing Techniques: Preliminary System Performance Results - Part 2

This paper presents the preliminary performance results of the artificial intelligence monitoring system in full operational mode using near real time acceleration data downlinked from the International Space Station. Preliminary microgravity environment characterization analysis result for the International Space Station (Increment-2), using the monitoring system is presented. Also, comparison between the system predicted performance based on ground test data for the US laboratory "Destiny" module and actual on-orbit performance, using measured acceleration data from the U.S. laboratory module of the International Space Station is presented. Finally, preliminary on-orbit disturbance magnitude levels are presented for the Experiment of Physics of Colloids in Space, which are compared with on ground test data. The ground test data for the Experiment of Physics of Colloids in Space were acquired from the Microgravity Emission Laboratory, located at the NASA Glenn Research Center, Cleveland, Ohio. The artificial intelligence was developed by the NASA Glenn Principal Investigator Microgravity Services Project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment of time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the principal investigator teams can monitor via a dynamic graphical display, implemented in Java, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, structural modes, etc., and decide whether or not to run their experiments, whenever that is an option, based on the acceleration magnitude and frequency sensitivity associated with that experiment. This monitoring system detects primarily the vibratory disturbance sources. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗
DOE OSTI2014

Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

47 OTHER INSTRUMENTATION↗
NASA NTRS2006

Real Time On-line Space Research Laboratory Environment Monitoring with Off-line Trend and Prediction Analysis

One of the responsibilities of the NASA Glenn Principal Investigator Microgravity Services is to support NASA sponsored investigators in the area of reduced-gravity acceleration data analysis, interpretation and the monitoring of the reduced-gravity environment on-board various carriers. With the International Space Station currently operational, a significant amount of acceleration data is being down-linked and processed on ground for both the space station onboard environment characterization (and verification) and scientific experiments. Therefore, to help principal investigator teams monitor the acceleration level on-board the International Space Station to avoid undesirable impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services developed an artificial intelligence monitoring system, which detects in near real time any change in the environment susceptible to affect onboard experiments. The main objective of the monitoring system is to help research teams identify the vibratory disturbances that are active at any instant of time onboard the International Space Station that might impact the environment in which their experiment is being conducted. The monitoring system allows any space research scientist, at any location and at any time, to see the current acceleration level on-board the Space Station via the World Wide Web. From the NASA Glenn s Exploration Systems Division web site, research scientists can see in near real time the active disturbances, such as pumps, fans, compressor, crew exercise, re-boost, extra-vehicular activity, etc., and decide whether or not to continue operating or stopping (or making note of such activity for later correlation with science results) their experiments based on the g-level associated with that specific event. A dynamic graphical display accessible via the World Wide Web shows the status of all the vibratory disturbance activities with their degree of confidence as well as their g-level contribution to the environment. The system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many Increments of the space station for selected disturbance activities. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential system failure as well as for use by research scientists during their science results analysis. Examples of both real time on-line vibratory disturbance detection and off-line trend analysis are presented in this paper. Several soft computing techniques such as Kohonen s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗