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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 325 records · Page 18
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NASA NTRSDate not supplied

Control Effector Unsaturation Modification to the Cascading Generalized Inverse Control Allocation Algorithm

Control allocation has sufficiently progressed such that it is used in front-line fighter aircraft such as the F-18Superhornet and the F-35 Joint Strike Fighter. Published literature shows the F-35 utilizes Nonlinear Dynamic Inversion in conjunction with an Effector Blender that incorporates the Cascading Generalized Inverse control allocation algorithm. While the Cascading Generalized Inverse algorithm is one of the premier generalized inverse methods, it does suffer from three deficiencies. In particular, it suffers from an inability to achieve some desired outcomes, it intermittently provides non-optimal solutions and generally fails to preserve moment direction near maximal achievable moments. An effector unsaturation method based on a Scalar Difference Quadratic was first introduced and implemented on the iterative Prediction Method control allocation algorithm which was shown to consistently achieve optimal (weighted) control allocation solutions throughout the entire Attainable Moment Set while preserving desired moment direction. In this paper, the shortcomings of the Cascading Generalized Inverse algorithm are addressed by augmenting the baseline algorithm with Scalar Difference Quadratic unsaturation identification and location at each iteration. Numerical case studies demonstrate that the Modified Cascading Generalized Inverse algorithm resolves the aforementioned deficiencies.

Michael J Acheson↗
NASA NTRS1981

An evaluation of ISOCLS and CLASSY clustering algorithms for forest classification in northern Idaho

Both the iterative self-organizing clustering system (ISOCLS) and the CLASSY algorithms were applied to forest and nonforest classes for one 1:24,000 quadrangle map of northern Idaho and the classification and mapping accuracies were evaluated with 1:30,000 color infrared aerial photography. Confusion matrices for the two clustering algorithms were generated and studied to determine which is most applicable to forest and rangeland inventories in future projects. In an unsupervised mode, ISOCLS requires many trial-and-error runs to find the proper parameters to separate desired information classes. CLASSY tells more in a single run concerning the classes that can be separated, shows more promise for forest stratification than ISOCLS, and shows more promise for consistency. One major drawback to CLASSY is that important forest and range classes that are smaller than a minimum cluster size will be combined with other classes. The algorithm requires so much computer storage that only data sets as small as a quadrangle can be used at one time.

Werth, L. F.↗
NASA NTRS2010

Fast Solution in Sparse LDA for Binary Classification

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Moghaddam, Baback↗
NASA NTRS1989

Parallel conjugate gradient algorithms for manipulator dynamic simulation

Parallel conjugate gradient algorithms for the computation of multibody dynamics are developed for the specialized case of a robot manipulator. For an n-dimensional positive-definite linear system, the Classical Conjugate Gradient (CCG) algorithms are guaranteed to converge in n iterations, each with a computation cost of O(n); this leads to a total computational cost of O(n sq) on a serial processor. A conjugate gradient algorithms is presented that provide greater efficiency using a preconditioner, which reduces the number of iterations required, and by exploiting parallelism, which reduces the cost of each iteration. Two Preconditioned Conjugate Gradient (PCG) algorithms are proposed which respectively use a diagonal and a tridiagonal matrix, composed of the diagonal and tridiagonal elements of the mass matrix, as preconditioners. Parallel algorithms are developed to compute the preconditioners and their inversions in O(log sub 2 n) steps using n processors. A parallel algorithm is also presented which, on the same architecture, achieves the computational time of O(log sub 2 n) for each iteration. Simulation results for a seven degree-of-freedom manipulator are presented. Variants of the proposed algorithms are also developed which can be efficiently implemented on the Robot Mathematics Processor (RMP).

Fijany, Amir↗
NASA NTRS1972

The computation of optimal control programmes using a modified successive sweep method.

A second-order method for numerically solving control optimization problems has been developed. The method, referred to as the modified sweep method (MSM), differs from the successive sweep method (SSM) proposed by McReynolds and Bryson (1965) in that the conditions for local control optimality are used to determine the control as an explicit function of the state variables and time. The control is eliminated from the problem and the solution to the resulting two-point boundary value problem can be obtained by linear perturbation methods. The modified sweep method proposed here uncouples the perturbation equations for the state variables and the Lagrange multipliers by using a generalized matrix-Riccati transformation of variables. The resulting algorithm for the numerical iteration process is concerned with determining the initial values of a set of Lagrange multipliers rather than correcting a numerical control programme over the entire time interval of interest.

Colunga, D.↗
NASA NTRS1979

Flight test of a VTOL digital autoland system along complex trajectories

The objective of this research is the development and flight test of a digital multi-rate VTOL autoland system. This autoland system provided the NASA/Army Fly-by-Wire CH-47 helicopter with an automatic 4-D trajectory following and landing capability. Flight tests were conducted along complex trajectories with descending, turning, and decelerating segments. The flight software consists of a trajectory generator and guidance algorithm operating at two iterations per second and a set of sensor filters and an advanced controller operating at 10 iterations per second. This low iteration rate digital controller has a proportional-integral structure that provides an autotrim function. The control gains, scheduled as a function of flight condition, are updated every two seconds. All flight software, except input/output routines, are coded in FORTRAN using floating point arithmetic and are exercised in a ROLM 1664 flight computer. The flight software, the ground and flight hardware, the autoland system's position tracking performance, and the controller's velocity and heading tracking performance are presented.

Downing, D. R.↗
NASA NTRS1979

Sequential triangulation of orbital photography

The feasibility of structuring the satellite photogrammetric triangulation as an iterative Extended Kalman estimation algorithm is demonstrated. Comparative numerical results of the sequential against batch estimation algorithm are presented. Difficulty of accurately modeling of the attitude motion is overcome by utilizing the on-board angular rate measurements. Solutions of the differential equations and the evaluation of state transition matrix are carried out numerically.

Rajan, M.↗
NASA NTRS1981

Minimum-fuel aerodynamic orbital plane change maneuvers

Several minimum-fuel, aerodynamically controlled, orbital plane change problems are formulated and solved as optimal control problems. A gradient projection algorithm is used to iteratively modify both the control functions, angle of attack and bank angle, and two control parameters to obtain the optimal trajectory. The atmospheric flight profile is combined with two Keplerian (two-body vacuum flight) arcs so that a complete orbit-to-orbit analysis results. The vehicle used in this investigation is the Space Transportation System Shuttle Orbiter. The effects of heat load constraint level and plane change angle are analysed.

Joosten, B. K.↗
NASA NTRS1983

Planning Transport and Manufacturing for Lowest Cost

A method applicable to transportation and manufacturing. New algorithm alleviates some mathematical difficulties of planning segmented trajectories for lowest cost. Algorithm involves modified Newtonian iterative method in which periapse times, closest approach distances, and orientations of approach hyperbolas serves as independent variables.

Damario, L. A.↗
NASA NTRS1982

Equivalent source modeling of the main field using MAGSAT data

An iterative least squares estimation algorithm with the capability for including a priori statistical information was implemented to recover multiple magnetic dipole models of the Earth's main magnetic field. The dipoles are fixed to a specified radius at or below the core-mantle boundary and centered on equal area blocks. The algorithm can solve for dipole magnitudes only (fixed orientations), or allow full freedom of orientation and solve for vector components. External field parameters and observatory anomaly biases can also be estimated simultaneously. Time dependence is modeled using first time derivatives for dipole vector components. Single-epoch and time dependent dipole models are derived using MAGSAT and observatory annul means data. Equivalent spherical harmonic representation are computed in closed form from the dipole models and compared with truncated spherical harmonic models estimated in the standard way from the same data sets. In particular, a 21 deg spatial resolution model based on 93 dipoles was computed based on observatory annual means data and a selected MAGSAT data set and was compared with candidate IGRF 1975 models and their 1980 secular variation.

Mayhew, M. A.↗
NASA NTRS1986

Shape Determination for Large Static Structures

Parameter and shape estimates updated from new measurements. Involves statistical structural analysis, statistical electromagneticfield analysis, filtering, measurement modeling, and iterative prediction/correction procedures. Estimating algorithms result from generalizations of Kalman statistical-filter theory.

Rodriguez, G.↗
NASA NTRS1986

An optimization model for the US Air-Traffic System

A systematic approach for monitoring U.S. air traffic was developed in the context of system-wide planning and control. Towards this end, a network optimization model with nonlinear objectives was chosen as the central element in the planning/control system. The network representation was selected because: (1) it provides a comprehensive structure for depicting essential aspects of the air traffic system, (2) it can be solved efficiently for large scale problems, and (3) the design can be easily communicated to non-technical users through computer graphics. Briefly, the network planning models consider the flow of traffic through a graph as the basic structure. Nodes depict locations and time periods for either individual planes or for aggregated groups of airplanes. Arcs define variables as actual airplanes flying through space or as delays across time periods. As such, a special case of the network can be used to model the so called flow control problem. Due to the large number of interacting variables and the difficulty in subdividing the problem into relatively independent subproblems, an integrated model was designed which will depict the entire high level (above 29000 feet) jet route system for the 48 contiguous states in the U.S. As a first step in demonstrating the concept's feasibility a nonlinear risk/cost model was developed for the Indianapolis Airspace. The nonlinear network program --NLPNETG-- was employed in solving the resulting test cases. This optimization program uses the Truncated-Newton method (quadratic approximation) for determining the search direction at each iteration in the nonlinear algorithm. It was shown that aircraft could be re-routed in an optimal fashion whenever traffic congestion increased beyond an acceptable level, as measured by the nonlinear risk function.

Mulvey, J. M.↗
NASA NTRS1986

Numerical prediction of cold turbulent flow in combustor configurations with different centerbody flame holders

A numerical study of cold turbulent flow in a combustor containing a centerbody flame holder is presented. The axisymmetric Navier-Stokes equations incorporating a k-epsilon turbulence model were solved in a nonorthogonal curvilinear coordinate system. The finite volume method, applied to a staggered grid system, was used to discretize the differential equations. These finite differenced equations were then solved iteratively utilizing the SIMPLE algorithm described by Patankar (1980). Solutions were obtained for two combustor duct geometries with various centerbody flame holders which included a disk, a cone, and a sphere. The extent of mixing due to these bodies was evaluated. A comparison with previously obtained experimental data yielded moderately good agreement.

Yung, C. N.↗
NASA NTRS1989

Low-pass spatial filtering of satellite radar data

Thirty-four low-pass spatial filter treatments were applied to a multi-angle SIR-B data set to reduce speckle effects and improve classification performance. These treatments were based on four algorithms: square mean, separable mean, square median, and separable recursive median. The filtered images were evaluated using both quantitative and qualitative techniques. It was determined that the square median algorithm implemented at two iterations with a window size of 3 by 3 produced the best overall results with the 28.5-m SIR-B data.

Mueller, Paul W.↗
NASA NTRS1989

Novel techniques for data decomposition and load balancing for parallel processing of vision systems: Implementation and evaluation using a motion estimation system

Computer vision systems employ a sequence of vision algorithms in which the output of an algorithm is the input of the next algorithm in the sequence. Algorithms that constitute such systems exhibit vastly different computational characteristics, and therefore, require different data decomposition techniques and efficient load balancing techniques for parallel implementation. However, since the input data for a task is produced as the output data of the previous task, this information can be exploited to perform knowledge based data decomposition and load balancing. Presented here are algorithms for a motion estimation system. The motion estimation is based on the point correspondence between the involved images which are a sequence of stereo image pairs. Researchers propose algorithms to obtain point correspondences by matching feature points among stereo image pairs at any two consecutive time instants. Furthermore, the proposed algorithms employ non-iterative procedures, which results in saving considerable amounts of computation time. The system consists of the following steps: (1) extraction of features; (2) stereo match of images in one time instant; (3) time match of images from consecutive time instants; (4) stereo match to compute final unambiguous points; and (5) computation of motion parameters.

Choudhary, Alok Nidhi↗
NASA NTRS1989

Harmonic analysis of spacecraft power systems using a personal computer

The effects that nonlinear devices such as ac/dc converters, HVDC transmission links, and motor drives have on spacecraft power systems are discussed. The nonsinusoidal currents, along with the corresponding voltages, are calculated by a harmonic power flow which decouples and solves for each harmonic component individually using an iterative Newton-Raphson algorithm. The sparsity of the harmonic equations and the overall Jacobian matrix is used to an advantage in terms of saving computer memory space and in terms of reducing computation time. The algorithm could also be modified to analyze each harmonic separately instead of all at the same time.

Williamson, Frank↗
NASA NTRS1991

Computing Gravitational Bumps From Repeating-Orbit Data

Iterative, least-squares algorithm efficiently computes estimates of both position errors indicative of irregularities in gravitational field of Earth and trajectory of satellite in orbit repeating along same ground track. Exploits sparse-matrix techniques. Useful in surveying, navigation, and geophysical research. Particularly useful for processing data on trajectory of satellite in low orbit tracked via Global Positioning System (GPS).

Wu, Jiun-Tsong↗
NASA NTRS1991

A tool for interactive exploration of a hierarchical segmentation

Hierarchical segmentation is discussed as a form of region growing in which the sequence of merges is controlled by a 'best merge first' principle, and a record of the region merging sequence is often retained for later analysis. In the author's massively parallel implementation of hierarchical segmentation, which he calls iterative parallel region growing (IPRG), a set of directional edge maps are used to store the region merging sequence information. An iteractive tool is described that allows an analyst to fully explore a hierarchical segmentation, potentially producing an image segmentation that is a combination of segmentations produced at several different iterations of the IPRG algorithm. A potential method for automatically producing such segmentations is also discussed.

Tilton, James C.↗