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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

CLASSY: An adaptive maximum likelihood clustering algorithm

The CLASSY clustering method alternates maximum likelihood iterative techniques for estimating the parameters of a mixture distribution with an adaptive procedure for splitting, combining, and eliminating the resultant components of the mixture. The adaptive procedure is based on maximizing the fit of a mixture of multivariate normal distributions to the observed data using its first through fourth central moments. It generates estimates of the number of multivariate normal components in the mixture as well as the proportion, mean vector, and covariance matrix for each component. The basic mathematical model for CLASSY and the actual operation of the algorithm as currently implemented are described. Results of applying CLASSY to real and simulated LANDSAT data are presented and compared with those generated by the iterative self-organizing clustering system algorithm on the same data sets.

Lennington, R. K.↗
NASA NTRS1988

Adaptive Control For Space-Station Joints

Input gain weighting added to existing adaptive-control algorithm. Report discusses concept for adaptive control of rotations of various appended structures about joints on space station, with suppression of vibrations in both station and appendages. Concept relevant to such terrestrial applications as control of aircraft, suppression of vibrations in large buildings, and robotics.

Ih, Che-Hang Charles↗
NASA NTRS1981

Self-adaptive predictor-corrector algorithm for static nonlinear structural analysis

A multiphase selfadaptive predictor corrector type algorithm was developed. This algorithm enables the solution of highly nonlinear structural responses including kinematic, kinetic and material effects as well as pro/post buckling behavior. The strategy involves three main phases: (1) the use of a warpable hyperelliptic constraint surface which serves to upperbound dependent iterate excursions during successive incremental Newton Ramphson (INR) type iterations; (20 uses an energy constraint to scale the generation of successive iterates so as to maintain the appropriate form of local convergence behavior; (3) the use of quality of convergence checks which enable various self adaptive modifications of the algorithmic structure when necessary. The restructuring is achieved by tightening various conditioning parameters as well as switch to different algorithmic levels to improve the convergence process. The capabilities of the procedure to handle various types of static nonlinear structural behavior are illustrated.

Padovan, J.↗
NASA NTRS1967

Study of a global search algorithm for optimal control.

Adaptive random search algorithm utilizing boundary cost-function hypersurfaces measurement to implement Pontryagin maximum principle, discussing hybrid computer use, iterative solution and convergence properties

Brocker, D. H.↗
NASA NTRS1982

An adaptive tracking observer for failure-detection systems

The design problem of adaptive observers applied to linear, constant and variable parameters, multi-input, multi-output systems, is considered. It is shown that, in order to keep the observer's (or Kalman filter) false-alarm rate (FAR) under a certain specified value, it is necessary to have an acceptable proper matching between the observer (or KF) model and the system parameters. An adaptive observer algorithm is introduced in order to maintain desired system-observer model matching, despite initial mismatching and/or system parameter variations. Only a properly designed adaptive observer is able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability and FAR. Conditions for convergence for the adaptive process were obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors and accurate and fast parameter identification, in both deterministic and stochastic cases.

Sidar, M.↗
NASA NTRS1982

Design of analytical failure detection using secondary observers

The problem of designing analytical failure-detection systems (FDS) for sensors and actuators, using observers, is addressed. The use of observers in FDS is related to the examination of the n-dimensional observer error vector which carries the necessary information on possible failures. The problem is that in practical systems, in which only some of the components of the state vector are measured, one has access only to the m-dimensional observer-output error vector, with m or = to n. In order to cope with these cases, a secondary observer is synthesized to reconstruct the entire observer-error vector from the observer output error vector. This approach leads toward the design of highly sensitive and reliable FDS, with the possibility of obtaining a unique fingerprint for every possible failure. In order to keep the observer's (or Kalman filter) false-alarm rate under a certain specified value, it is necessary to have an acceptable matching between the observer (or Kalman filter) models and the system parameters. A previously developed adaptive observer algorithm is used to maintain the desired system-observer model matching, despite initial mismatching or system parameter variations. Conditions for convergence for the adaptive process are obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors, while accurate and fast parameter identification, in both deterministic and stochastic cases, is obtained.

Sisar, M.↗
DOE OSTI2022

CEAZ: Accelerating Parallel I/O Via Hardware-Algorithm Co-Designed Adaptive Lossy Compression

As supercomputers continue to grow to exa-scale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also help us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ’s dual-quantization algorithm to our hardware use case. (4) We evaluate CEAC on five real-world datasets with both a single FPGA board and 256 nodes from Bridges2 supercomputer. Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2× of throughput and 9.6× of compression ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 32.7× and 31.4×, respectively.

Zhang, Chengming↗
NASA NTRS1986

Performance comparisons on spatial lattice algorithm and direct matrix inverse method with application to adaptive arrays processing

Lattice algorithm has been employed in numerous adaptive filtering applications such as speech analysis/synthesis, noise canceling, spectral analysis, and channel equalization. In this paper the application to adaptive-array processing is discussed. The advantages are fast convergence rate as well as computational accuracy independent of the noise and interference conditions. The results produced by this technique are compared to those obtained by the direct matrix inverse method.

An, S. H.↗
NASA NTRS1986

Adaptive control robustness in flexible aircraft application

This paper presents the application of a model reference adaptive control (MRAC) algorithm to flexible aircraft flight control. The algorithm is an adaptive version of the Command Generator Tracking (CGT) control technique. This technique forces a dynamic system to follow a reduced-order model, allowing it to cope with the problem of unmodeled dynamics. The studies were made via simulation, using for the plant an aircraft dynamic model similar to the B1 bomber. This model is of a large aircraft with a reasonable amount of structural flexibility. In particular, flight configurations were analyzed where the influence of the flexible modes make it difficult to control the aircraft. The results indicate that the algorithm has good robustness properties vis-a-vis unmodeled dynamics and can force the flexible aircraft to follow rigid body responses.

Sanchez, E. N.↗
NASA NTRS2009

Implementation of an Adaptive Controller System from Concept to Flight Test

The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) is used to test and develop these algorithms. Modifications to this airplane include adding canards and changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals include demonstration of revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions and advancement of neural-network-based flight control technology for new aerospace system designs. This report presents an overview of the processes utilized to develop adaptive controller algorithms during a flight-test program, including a description of initial adaptive controller concepts and a discussion of modeling formulation and performance testing. Design finalization led to integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness; these are also discussed.

Larson, Richard R.↗
NASA NTRS2006

Functional Recovery of Analog Circuits at Extreme Temperatures

This paper describes a new reconfigurable analog array (RAA) architecture and integrated circuit (IC) used to map analog circuits that can adapt to extreme temperatures under programmable control. Algorithm-driven adaptation takes place on the RAA IC. The algorithms are implemented in a separate Field Programmable Gate Array (FPGA) IC, co-located with the RAA in the extreme temperature environment. The experiments demonstrate circuit adaptation over a wide temperature range, from extremely low temperature of -180 C to high 120 C.

self-recovery↗
NASA NTRS2006

Temperature-adaptive Circuits on Reconfigurable Analog Arrays

This paper describes a new reconfigurable analog array (MA) architecture and integrated circuit (IC) used to map analog circuits that can adapt to extreme temperatures under programmable control. Algorithm-driven adaptation takes place on the RAA IC. The algorithms are implemented in a separate Field Programmable Gate Array (FPGA) IC, co-located with the RAA in the extreme temperature environment. The experiments demonstrate circuit adaptation over a wide temperature range, from extremely low temperature of -180 C to high 120 C.

self recovery↗
NASA NTRS2002

Probabilistic #D data fusion for multiresolution surface generation

In this paper we present an algorithm for adaptive resolution integration of 3D data collected from multiple distributed sensors. The input to the algorithm is a set of 3D surface points and associated sensor models. Using a probabilistic rule, a surface probability function is generated that represents the probability that a particular volume of space contains the surface. The surface probability function is represented using an octree data structure; regions of space with samples of large conariance are stored at a coarser level than regions of space containing samples with smaller covariance. The algorithm outputs an adaptive resolution surface generated by connecting points that lie on the ridge of surface probability with triangles scaled to match the local discretization of space given by the algorithm, we present results from 3D data generated by scanning lidar and structure from motion.

3D data fusion multiresolution surface generation ↗
NASA NTRS1988

Adaptive implicit-explicit finite element algorithms for fluid mechanics problems

The adaptive implicit-explicit (AIE) approach is presented for the finite-element solution of various problems in computational fluid mechanics. In the AIE approach, the elements are dynamically (adaptively) arranged into differently treated groups. The differences in treatment could be based on considerations such as the cost efficiency, the type of spatial or temporal discretization employed, the choice of field equations, etc. Several numerical tests are performed to demonstrate that this approach can achieve substantial savings in CPU time and memory.

Tezduyar, T. E.↗
NASA NTRS1996

Adaptive Control Strategies for Flexible Robotic Arm

The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.

Bialasiewicz, Jan T.↗
NASA NTRS1997

Adaptive Load-Balancing Algorithms Using Symmetric Broadcast Networks

In a distributed-computing environment, it is important to ensure that the processor workloads are adequately balanced. Among numerous load-balancing algorithms, a unique approach due to Dam and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three novel SBN-based load-balancing algorithms, and implement them on an SP2. A thorough experimental study with Poisson-distributed synthetic loads demonstrates that these algorithms are very effective in balancing system load while minimizing processor idle time. They also compare favorably with several other existing load-balancing techniques. Additional experiments performed with real data demonstrate that the SBN approach is effective in adaptive computational science and engineering applications where dynamic load balancing is extremely crucial.

Das, Sajal K.↗
NASA NTRS1972

Demonstration of the use of ADAPT to derive predictive maintenance algorithms for the KSC central heat plant

The Avco Data Analysis and Prediction Techniques (ADAPT) were employed to determine laws capable of detecting failures in a heat plant up to three days in advance of the occurrence of the failure. The projected performance of algorithms yielded a detection probability of 90% with false alarm rates of the order of 1 per year for a sample rate of 1 per day with each detection, followed by 3 hourly samplings. This performance was verified on 173 independent test cases. The program also demonstrated diagnostic algorithms and the ability to predict the time of failure to approximately plus or minus 8 hours up to three days in advance of the failure. The ADAPT programs produce simple algorithms which have a unique possibility of a relatively low cost updating procedure. The algorithms were implemented on general purpose computers at Kennedy Space Flight Center and tested against current data.

Hunter, H. E.↗