Algebraic criterion for absolute stability, optimality, and passivity of dynamic systems
Dynamic systems absolute stability, optimality and passivity algebraic criterion in terms of real even polynomial coefficients
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Dynamic systems absolute stability, optimality and passivity algebraic criterion in terms of real even polynomial coefficients
Dynamic response characteristics of time-varying linear systems
Dynamic responses of biophysical systems - performance characteristics
Internal human systems dynamic response - automatic control theory, temperature control, cardiovascular system, hormone dynamics, and behavioral organization
Regulation and control analysis of some internal human systems dynamics
Data Systems Dynamic Simulation is a simulation system designed to reduce cost and time and increase the confidence and comprehensiveness of Data Systems Simulation. It is designed to simulate large data processing and communications systems from end-to-end or by subsystem. Those features relevant to system timing, control, sizing, personnel support activities, cost and external influences are modeled. Emphasis is placed on ease of use, comprehensive system performance measures, and extensive post simulation analysis capability. The system has been used to support trade studies of the NASA data system needs in the 1985 to 1990 time frame.
Attractors in dynamic systems - Liapunov and plain stability of compact invariant set
Generalized recurrence in dynamical systems with aid of continuous real valued function on phase space
Equation for linear dynamic systems simplification applicable to all cases irrespective of nature of eigenvalues and eigenvectors
Filtering for nonlinear dynamical systems with white Gaussian noise processes
A symbolic computation technique for determining the eigenvalues of dynamical systems is described wherein algebraic operations, symbolic differentiation, matrix formulation and inversion, etc., can be performed on a digital computer equipped with a formula-manipulation compiler. An example is included that demonstrates the facility with which the system dynamics matrix and the control distribution matrix from the state space formulation of the equations of motion can be processed to obtain eigenvalue loci as a function of a system parameter. The example chosen to demonstrate the technique is a fourth-order system representing the longitudinal response of a DC 8 aircraft to elevator inputs. This simplified system has two dominant modes, one of which is lightly damped and the other well damped. The loci may be used to determine the value of the controlling parameter that satisfied design requirements. The results were obtained using the MACSYMA symbolic manipulation system.
Directed graphs are associated with dynamic systems in order to determine in any given system if each state can be reached by at least one input (input reachability), or can each state reach at least one output (output reachability). Then, the structural perturbations of a dynamic system are identified as lines or points removals from the corresponding digraph, and a system is considered vulnerable at those lines or points of the digraph whose removal destroys its input or output reachability. A suitable framework is formulated for resolving the problems of reachability and vulnerability which applies to both linear and nonlinear systems alike.
System dynamics and control analysis of tungsten water moderated nuclear rocket
Parameter analysis of dynamical systems by matrix approach, using n-dimensional parameter space
Invariant hyperplanes for linear dynamical systems
Extended dynamical system to describe operators in study of stability
Extended dynamical systems in Banach space and use of invariance principle for stability theory of partial differential equations
Invariant hyperplanes for linear dynamical systems, discussing geometric properties and relation to controllability and observability