On ''A method for simplifying linear dynamic systems.''
Equation for linear dynamic systems simplification applicable to all cases irrespective of nature of eigenvalues and eigenvectors
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Equation for linear dynamic systems simplification applicable to all cases irrespective of nature of eigenvalues and eigenvectors
Invariant hyperplanes for linear dynamical systems
Invariant hyperplanes for linear dynamical systems, discussing geometric properties and relation to controllability and observability
Approximate model for simplification of linear dynamic system, neglecting effect of higher order time constants
Identification and control of linear dynamic systems with unknown parameters
Dynamic response characteristics of time-varying linear systems
Invariant subspaces, controllability and observability of linear dynamic systems
The properties of reachable sets for linear dynamical systems for specified control sets are discussed. Iterative procedures for determining numerical approximations of the reachable set are suggested and methods of obtaining an admissible control function which transfers an initial state to as near a prescribed target as possible is described. The problem of reachability with multiple control constraints is discussed and certain aspects of reachability for time-invariant systems with adjustable parameters is considered.
Analytical measure of quality of controllability for linear dynamic system and computational procedure for maximizing adjustable structural parameters
It is assumed that the system matrices of a stationary linear dynamical system were parametrized by a set of unknown parameters. The question considered here is, when can such a set of unknown parameters be identified from the observed data? Conditions for the local identifiability of a parametrization are derived in three situations: (1) when input/output observations are made, (2) when there exists an unknown feedback matrix in the system and (3) when the system is assumed to be driven by white noise and only output observations are made. Also a sufficient condition for global identifiability is derived.
Simultaneous estimation of state and noise statistics in linear dynamical systems using optimal procedure
Existence and construction of modified L lag inverses solution method for linear dynamical systems
In various problems in structural dynamics, the eigenvalues of a linear system depend on a characteristic parameter of the system. Under certain conditions, two eigenvalues of the system approach each other as the characteristic parameter is varied, leading to modal interaction. In a system with conservative coupling, the two eigenvalues eventually repel each other, leading to the curve veering effect. In a system with nonconservative coupling, the eigenvalues continue to attract each other, eventually colliding, leading to eigenvalue degeneracy. Modal interaction is studied in linear systems with conservative and nonconservative coupling using singularity theory, sometimes known as catastrophe theory. The main result is this: eigenvalue degeneracy is a cause of instability; in systems with conservative coupling, it induces only geometric instability, whereas in systems with nonconservative coupling, eigenvalue degeneracy induces both geometric and elastic instability. Illustrative examples of mechanical systems are given.
Determining the dynamic response characteristics of time-varying linear systems
Multiparameter optimum damping for harmonically excited linear stable strictly dissipative n degrees of freedom system, locating multivariable saddle points
A method is derived for digital computer simulation of linear time-invariant systems when the insignificant eigenvalues involved in such systems are eliminated by an ALSAP root removal technique. The method is applied to a thirteenth-order dynamic system representing a passive RLC network.
Consider the situation in which the unknown parameters of a stationary linear system may be parametrized by a set of unknown parameters. The question thus arises of when such a set of parameters can be uniquely identified on the basis of observed data. This problem is considered here both in the case of input and output observations and in the case of output observations in the presence of a white noise input. Conditions for local identifiability are derived for both situations and a sufficient condition for global identifiability is given for the former situation, i.e., when simultaneous input and output observations are available.
Nonlinear dynamical systems research on systems stability, invariance principles, Liapunov functions, and Volterra and functional integral equations