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In many control designs the error signal is split into parallel channels which are tailored to be significant over distinct frequency ranges and are then recombined. Often, at least one channel contains nonlinear elements, The most common example is a PID controller with an anti-windup nonlinearity in the I-channel. A more general example using higher-order compensators is presented, which is a thermal controller for a spacecraft-mounted telescope.
Nonlinearity effects in optical data processing, and FORTRAN program for analyzing nonlinearities in spectroscopic photographic plates
Linear problems involving linear and nonlinear systems of differential equations, proving theorems for nonlinear differential equation systems
Nonlinear dynamics discrete estimation by computational algorithm involving nonlinear algebraic equations
Continuous time nonlinear dynamical system states variables estimated suboptimally from discrete noisy measurements with second order nonlinear filter, deriving dynamical equations
Nonlinear boundary layer for shells of revolution, deriving nonlinear differential equations for infinitesimal strains and small rotation
Continuous time nonlinear dynamical system states variables estimated suboptimally from discrete noisy measurements with second order nonlinear filter, deriving dynamical equations
Electron anomalous magnetic moment effect on nonlinear Lagrangian of electromagnetic field, deriving additional nonlinear corrective term
Optimal nonlinear filtering for nonlinear dynamic system subjected to additive white Gaussian noise with corrupted observations, using successive linearization scheme
Parameter plane method of stability analysis applied to spacecraft attitude control system with two nonlinearities related by nonlinear differential equation
Program predicts expected deformations and stresses in nonlinear simple geometric structures subjected to high-impact loading. Technique is based on node-wise predictor-corrector approach and requires moderate computer storage and run time for most problems. Program extends to include physical and geometrical nonlinearities.
Test configuration measures harmonic signal amplitudes generated in nonlinear resistance. Vacuum-type voltmeter measures low frequency sinusoidal input signal amplitude and wave-analyzer measures amplitude of harmonic signals generated in junction. Input signal harmonics amplitude must not exceed that of harmonics generated in nonlinear resistance.
Perturbation method extension for nonlinear panel flutter to include fifth-order nonlinear terms effect, flutter-buckling interaction, and small damping terms
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
The possibility of using on-line signal statistics to detect electronic equipment nonlinearities is discussed. The results of an investigation using Gaussian statistics are presented, and a nonlinearity test that uses ratios of the moments of a Gaussian random variable is developed and discussed. An outline for further investigation is presented.
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
A procedure cast in a form appropriate to the finite element method is presented for geometrically nonlinear prebuckling and postbuckling structural analysis, including the identification of snap-through type of buckling. The principal features of this procedure are the use of direct iteration for solution of the nonlinear algebraic equations in the prebuckling range, an interpolation scheme for determination of the initial bifurcation point, a perturbation method in definition of the load-displacement behavior through the postbuckling regime, and extrapolation in determination of the limit point for snap-through buckling. Three numerical examples are presented in illustration of the procedure and in comparison with alternative approaches.
A variety of techniques is available for estimating the states of nonlinear dynamic systems from noisy data. The differences among several of these procedures in the presence of small dynamic and observational nonlinearities are investigated. Four discrete estimation algorithms are analyzed. The first is a strictly least square estimator, while the others are recursive algorithms similar to the Kalman filter used for estimating the states of linear systems. A group of analytic expressions is developed for the mean and covariance of the error in each of these estimators so that they may be compared without lengthy Monte Carlo simulations. The covariance expressions show that, to first order, all the estimators have the same covariance. Expressions for the means show that each estimator has a different bias. Several examples are carried out demonstrating that the relative magnitudes of the bias errors in the various estimators can be a strong function of such parameters as initial covariances and number of data points. Under some circumstances, more complicated algorithms can have larger biases than smaller ones.