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At least 73 records · Page 4

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.↗

Intelligent Planning and Scheduling for Controlled Life Support Systems

Planning in Controlled Ecological Life Support Systems (CELSS) requires special look ahead capabilities due to the complex and long-term dynamic behavior of biological systems. This project characterizes the behavior of CELSS, identifies the requirements of intelligent planning systems for CELSS, proposes the decomposition of the planning task into short-term and long-term planning, and studies the crop scheduling problem as an initial approach to long-term planning. CELSS is studied in the realm of Chaos. The amount of biomass in the system is modeled using a bounded quadratic iterator. The results suggests that closed ecological systems can exhibit periodic behavior when imposed external or artificial control. The main characteristics of CELSS from the planning and scheduling perspective are discussed and requirements for planning systems are given. Crop scheduling problem is identified as an important component of the required long-term lookahead capabilities of a CELSS planner. The main characteristics of crop scheduling are described and a model is proposed to represent the problem. A surrogate measure of the probability of survival is developed. The measure reflects the absolute deviation of the vital reservoir levels from their nominal values. The solution space is generated using a probability distribution which captures both knowledge about the system and the current state of affairs at each decision epoch. This probability distribution is used in the context of an evolution paradigm. The concepts developed serve as the basis for the development of a simple crop scheduling tool which is used to demonstrate its usefulness in the design and operation of CELSS.

Leon, V. Jorge↗

Fiber-Optic Pressure Sensor With Dynamic Demodulation Developed

Researchers at the NASA Glenn Research Center developed in-house a method to detect pressure fluctuations using a fiber-optic sensor and dynamic signal processing. This work was in support of the Intelligent Systems Controls and Operations project under NASA's Information Technology Base Research Program. We constructed an optical pressure sensor by attaching a fiber-optic Bragg grating to a flexible membrane and then adhering the membrane to one end of a small cylinder. The other end of the cylinder was left open and exposed to pressure variations from a pulsed air jet. These pressure variations flexed the membrane, inducing a strain in the fiber-optic grating. This strain was read out optically with a dynamic spectrometer to record changes in the wavelength of light reflected from the grating. The dynamic spectrometer was built in-house to detect very small wavelength shifts induced by the pressure fluctuations. The spectrometer is an unbalanced interferometer specifically designed for maximum sensitivity to wavelength shifts. An optimum pathlength difference, which was determined empirically, resulted in a 14-percent sensitivity improvement over theoretically predicted path-length differences. This difference is suspected to be from uncertainty about the spectral power difference of the signal reflected from the Bragg grating. The figure shows the output of the dynamic spectrometer as the sensor was exposed to a nominally 2-kPa peak-to-peak square-wave pressure fluctuation. Good tracking, sensitivity, and signal-to-noise ratios are evident even though the sensor was constructed as a proof-of-concept and was not optimized in any way. Therefore the fiber-optic Bragg grating, which is normally considered a good candidate as a strain or temperature sensor, also has been shown to be a good candidate for a dynamic pressure sensor.

Lekki, John D.↗

Automatic Management of Parallel and Distributed System Resources

Viewgraphs on automatic management of parallel and distributed system resources are presented. Topics covered include: parallel applications; intelligent management of multiprocessing systems; performance evaluation of parallel architecture; dynamic concurrent programs; compiler-directed system approach; lattice gaseous cellular automata; and sparse matrix Cholesky factorization.

Yan, Jerry↗

Droplet-surface Impingement Dynamics for Intelligent Spray Design

Spray cooling has high potential in thermal management and life support systems by overcoming the deleterious effect of microgravity upon two-phase heat transfer. In particular spray cooling offers several advantages in heat flux removal that include the following: 1. By maintaining a wetted surface, spray droplets impinge upon a thin fluid film rather than a dry solid surface 2. Most heat transfer surfaces will not be smooth but rough. Roughness can enhance conductive cooling, aid liquid removal by flow channeling. 3. Spray momentum can be used to a) substitute for gravity delivering fluid to the surface, b) prevent local dryout and potential thermal runaway and c) facilitate liquid and vapor removal. Yet high momentum results in high We and Re numbers characterizing the individual spray droplets. Beyond an impingement threshold, droplets splash rather than spread. Heat flux declines and spray cooling efficiency can markedly decrease. Accordingly we are investigating droplet impingement upon a) dry solid surfaces, b) fluid films, c) rough surfaces and determining splashing thresholds and relationships for both dry surfaces and those covered by fluid films. We are presently developing engineering correlations delineating the boundary between splashing and non-splashing regions.

VanderWal, Randy L.↗

Droplet-Surface Impingement Dynamics for Intelligent Spray Design

Spray cooling has high potential in thermal management and life support systems by overcoming the deleterious effect of microgravity upon two-phase heat transfer. In particular spray cooling offers several advantages in heat flux removal that include the following: 1) By maintaining a wetted surface, spray droplets impinge upon a thin fluid film rather than a dry solid surface; 2. Most heat transfer surfaces will not be smooth but rough. Roughness can enhance conductive cooling, aid liquid removal by flow channeling; and 3. Spray momentum can be used to a) substitute for gravity delivering fluid to the surface, b) prevent local dryout and potential thermal runaway and c) facilitate liquid and vapor removal. Yet high momentum results in high We and Re numbers characterizing the individual spray droplets. Beyond an impingement threshold, droplets splash rather than spread. Heat flux declines and spray cooling efficiency can markedly decrease. Accordingly we are investigating droplet impingement upon a) dry solid surfaces, b) fluid films, c) rough surfaces and determining splashing thresholds and relationships for both dry surfaces and those covered by fluid films. We are presently developing engineering correlations delineating the boundary between splashing and non-splashing regions. Determining the splash/non-splash boundary is important for many practical applications. Coating and cooling processes would each benefit from near-term empirical relations and subsequent models. Such demonstrations can guide theoretical development by providing definitive testing of its predictive capabilities. Thus, empirical relations describing the boundary between splash and non-splash are given for drops impinging upon a dry solid surface and upon a thin fluid film covering a similar surface. Analytical simplification of the power laws describing the boundary between the splash and non-splash regions yields insight into the engineering parameters governing the splash and non-splash outcomes of the fluid droplets. The power law correlation is shown separating the splashing versus non-splashing regions as developed for droplets impinging upon a dry solid surface. Splashing upon a dry surface is reasonably described by Ca greater than 0.85, reflecting the competing roles of surface tension and viscosity. The power law correlation is shown separating the splashing versus non-splashing regions as developed for droplets impinging upon a thin fluid film covering the solid surface. Splashing upon a thin fluid film, as described by v (pd/s) greater than 63, is governed by fluid density and surface tension, but is rather independent of viscosity. Finally, the data presented here suggests that a more direct dependence upon the surface tension and viscosity, given a better understanding of their interplay, would allow accurate description of the droplet-surface impacts for more complicated situations involving non-Newtonian fluids, specifically those exhibiting viscoelastic behavior.

Wal, Randy L. Vander↗

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.↗

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.↗

Physical Invariants of Intelligence

A program of research is dedicated to development of a mathematical formalism that could provide, among other things, means by which living systems could be distinguished from non-living ones. A major issue that arises in this research is the following question: What invariants of mathematical models of the physics of systems are (1) characteristic of the behaviors of intelligent living systems and (2) do not depend on specific features of material compositions heretofore considered to be characteristic of life? This research at earlier stages has been reported, albeit from different perspectives, in numerous previous NASA Tech Briefs articles. To recapitulate: One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Mathematical models of motor dynamics are used to simulate the observable physical behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. At the time of reporting the information for this article, the focus of this research was upon the following aspects of the formalism: Intelligence is considered to be a means by which a living system preserves itself and improves its ability to survive and is further considered to manifest itself in feedback from the mental dynamics to the motor dynamics. Because of the feedback from the mental dynamics, the motor dynamics attains quantum-like properties: The trajectory of the physical aspect of the system in the space of dynamical variables splits into a family of different trajectories, and each of those trajectories can be chosen with a probability prescribed by the mental dynamics. From a slightly different perspective, the mechanism of decision-making is feedback from the mental dynamics to the motor dynamics, and this mechanism provides a quantum-like collapse of a random motion into an appropriate deterministic state, such that entropy undergoes a pronounced decrease. The existence of this mechanism is considered to be an invariant of intelligent behavior of living systems, regardless of the origins and material compositions of the systems.

Zak, Michail↗

Collaboration Between NASA Centers of Excellence on Autonomous System Software Development

Software for space systems flight operations has its roots in the early days of the space program when computer systems were incapable of supporting highly complex and flexible control logic. Control systems relied on fast data acquisition and supervisory control from a roomful of systems engineers on the ground. Even though computer hardware and software has become many orders of magnitude more capable, space systems have largely adhered to this original paradigm In an effort to break this mold, Kennedy Space Center (KSC) has invested in the development of model-based diagnosis and control applications for ten years having broad experience in both ground and spacecraft systems and software. KSC has now partnered with Ames Research Center (ARC), NASA's Center of Excellence in Information Technology, to create a new paradigm for the control of dynamic space systems. ARC has developed model-based diagnosis and intelligent planning software that enables spacecraft to handle most routine problems automatically and allocate resources in a flexible way to realize mission objectives. ARC demonstrated the utility of onboard diagnosis and planning with an experiment aboard Deep Space I in 1999. This paper highlights the software control system collaboration between KSC and ARC. KSC has developed a Mars In-situ Resource Utilization testbed based on the Reverse Water Gas Shift (RWGS) reaction. This plant, built in KSC's Applied Chemistry Laboratory, is capable of producing the large amount of Oxygen that would be needed to support a Human Mars Mission. KSC and ARC are cooperating to develop an autonomous, fault-tolerant control system for RWGS to meet the need for autonomy on deep space missions. The paper will also describe how the new system software paradigm will be applied to Vehicle Health Monitoring, tested on the new X vehicles and integrated into future launch processing systems.

Goodrich, Charles H.↗

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.↗

Flight Results of the NF-15B Intelligent Flight Control System (IFCS) Aircraft with Adaptation to a Longitudinally Destabilized Plant

Adaptive flight control systems have the potential to be resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. The goal for the adaptive system is to provide an increase in survivability in the event that these extreme changes occur. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane. The adaptive element was incorporated into a dynamic inversion controller with explicit reference model-following. As a test the system was subjected to an abrupt change in plant stability simulating a destabilizing failure. Flight evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to stabilize the vehicle and reestablish good onboard reference model-following. Flight evaluation with the simulated destabilizing failure and adaptation engaged showed improvement in the vehicle stability margins. The convergent properties of this initial system warrant additional improvement since continued maneuvering caused continued adaptation change. Compared to the non-adaptive system the adaptive system provided better closed-loop behavior with improved matching of the onboard reference model. A detailed discussion of the flight results is presented.

Bosworth, John T.↗

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗

Intent inferencing by an intelligent operator's associate - A validation study

In the supervisory control of a complex, dynamic system, one potential form of aiding for the human operator is a computer-based operator's associate. The design philosophy of the operator's associate is that of 'amplifying' rather than automating human skills. In particular, the associate possesses understanding and control properties. Understanding allows it to infer operator intentions and thus form the basis for context-dependent advice and reminders; control properties allow the human operator to dynamically delegate individual tasks or subfunctions to the associate. This paper focuses on the design, implementation, and validation of the intent inferencing function. Two validation studies are described which empirically demonstrate the viability of the proposed approach to intent inferencing.

Jones, Patricia M.↗

Merging Intelligent Systems Technologies with CFD Analysis Strategies: Prototype Development at NASA Ames

A collaborative team of researchers from fields of Computational Fluid Dynamics (CFD), fluid physics, computer architectures, and computer science and knowledge engineering have begun work on a prototype system that addresses several of industry's concerns in using NASA-developed CFD codes as part of the design cycle. A major problem exists in the application of CFD technologies within the aeronautics design cycle due primarily to misunderstandings in the ranges of applicability of the various solver codes or turbulence models. Features that arise during the CFD solution process need to be discriminated and recognized as actual flow features with physical support in the geometry and flow conditions of the problem being solved, or as numerical or non-physical errors arising from mis-application of solver code and its parameters, gridding strategies, or discretization. interpolations. The fundamental concept is to develop an intelligent computational system that can accept the engineer's definition of the problem and construct an optimal CFD solution. To do this requires capturing both the knowledge of how to apply the various CFD tools and how to adapt the application of those tools to flow structures as they evolve during the flow simulation. Embedded within this adaptive system approach is the additional desire to automatically identify and quantify the quality of resolution of the pertinent flow structures, be they genuine or error-induced, and then to adjust the solution strategy accordingly. This paper discusses the status of that prototyping effort.

Thompson, David E.↗

An intelligent training system for payload-assist module deploys

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The Payload-Assist Module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system has, so far, proven to be a potentially valuable addition to the training tools available for training Flight Dynamics Officers in shuttle ground control. The authors are convinced that the basic structure of PD/ICAT can be extended to form a general architecture for intelligent training systems for training flight controllers and crew members in the performance of complex, mission-critical tasks.

Loftin, R. Bowen↗

An intelligent training system for payload-assist module deploys

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The Payload-Assist Module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system has, so far, proven to be a potentially valuable addition to the training tools available for training Flight Dynamics Officers in shuttle ground control. The authors are convinced that the basic structure of PD/ICAT can be extended to form a general architecture for intelligent training systems for training flight controllers and crew members in the performance of complex, mission-critical tasks.

Loftin, R. Bowen↗

Pilot interaction with automated airborne decision making systems

The use of advanced software engineering methods (e.g., from artificial intelligence) to aid aircraft crews in procedure selection and execution is investigated. Human problem solving in dynamic environments as effected by the human's level of knowledge of system operations is examined. Progress on the development of full scale simulation facilities is also discussed.

Rouse, W. B.↗