Linear System Assembly on GPU for an Inviscid Flux Miniapp
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Linear nonautonomous random system stability, presenting theorem, two corollaries and second order examples
Linear nonautonomous random system stability, presenting theorem, two corollaries and second order examples
Linear computer-controlled closed loop systems sensitivity analysis, deriving estimation error incremental covariance to demonstrate quality deterioration under perturbed initial conditions, parameters, etc
Linear networks fault detection and isolation using computerized test procedure and symbolic transfer function generation technique
An iterative method for solving nonsymmetric indefinite linear systems is proposed. The method involves the successive use of a modified version of the conjugate residual method. A numerical example is given to illustrate the method.
During the process of solving a mathematical model numerically, there is often a need to operate on a vector v by an operator which can be expressed as f(A) while A is NxN matrix (ex: exp(A), sin(A), A sup -1). Except for very simple matrices, it is impractical to construct the matrix f(A) explicitly. Usually an approximation to it is used. In the present research, an algorithm is developed which uses a polynomial approximation to f(A). It is reduced to a problem of approximating f(z) by a polynomial in z while z belongs to the domain D in the complex plane which includes all the eigenvalues of A. This problem of approximation is approached by interpolating the function f(z) in a certain set of points which is known to have some maximal properties. The approximation thus achieved is almost best. Implementing the algorithm to some practical problem is described. Since a solution to a linear system Ax = b is x= A sup -1 b, an iterative solution to it can be regarded as a polynomial approximation to f(A) = A sup -1. Implementing the algorithm in this case is also described.
A linear system with two sets of controls, one primary and the other redundant, is considered. A two level optimization procedure is used to control the system and to maintain maximal availability of the primary control.
Acausal linear systems are studied as well as their controllability and observability properties, and the weighting patterns that they realize. A complete classification is given of all minimal real analytic realizations of a given weighting pattern and of all minimal autonomous realizations of a stationary weighting pattern.
A method for modeling linear time-varying differential systems by linear time-invariant systems of lower order is proposed, extending the results obtained by Bierman (1972) by resolving such qualities as the model stability, various possible models of differing dimensions, and the uniqueness or nonuniqueness of the model coefficient matrix. In addition to the advantages cited by Heffes and Sarachik (1969) and Bierman, often by modeling a subsystem of a larger system it is possible to analyze the overall system behavior more easily, with resulting savings in computation time.
Linear time-varying systems transient responses prediction, using Krylov-Bogoliubov method and parameter plane analysis
The Linear Actuator System (LAS) is a major sub-system within the NASA Docking System (NDS). The NDS Block 1 will be used on the Boeing Crew Space Transportation (CST-100) system to achieve docking with the International Space Station. Critical functions in the Soft Capture aspect of docking are performed by the LAS, which implements the Soft Impact Mating and Attenuation Concept (SIMAC). This paper describes the general function of the LAS, the system's key requirements and technical challenges, and the development and qualification approach for the system.
The Linear Actuator System (LAS) is a major sub-system within the NASA Docking System (NDS). The NDS Block 1 will be used on the Boeing Crew Space Transportation (CST-100) system to achieve docking with the International Space Station. Critical functions in the Soft Capture aspect of docking are performed by the LAS. This paper describes the general function of the LAS, the system's key requirements and technical challenges, and the development and qualification approach for the system.
Linear continuous time delay feedback systems stability analysis, using root-locus and frequency response techniques
Linear state variable feedback to obtain asymptotic stabilization of linear dynamic systems
Stability of linear nonautonomous systems described by differential equations with time varying coefficients
Equation for linear dynamic systems simplification applicable to all cases irrespective of nature of eigenvalues and eigenvectors
Characterization of linear passive systems