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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

A modification to the Eigensystem Realization Algorithm (ERA) for modal parameter identification is presented in this paper. The ERA minimum order realization approach using singular value decomposition is combined with the philosophy of the Correlation Fit method in state space form such that response data correlations rather than actual response values are used for modal parameter identification. This new method, the ERA using data correlations (ERA/DC), reduces bias errors due to noise corruption significantly without the need for model overspecification. This method is tested using simulated five-degree-of-freedom system responses corrupted by measurement noise. It is found for this case that, when model overspecification is permitted and a minimum order solution obtained via singular value truncation, the results from the two methods are of similar quality.

Juang, Jer-Nan↗
NASA NTRS1973

Case studies in Aircraft Parameter Identification

Three case studies in Aircraft Parameter Identification using simulated data for X-22 VTOL aircraft and flight test data for HL-10 and M2/F3 lifting bodies. After a brief discussion of the previous techniques and their limitations, a technique based on the Maximum Likelihood criterion is described. The problems of identifiability and uniqueness in determining the Stability and Control derivatives from flight test data are discussed and several methods for alleviating these problems are presented. The flight test data is analyzed in several different ways for obtaining physically meaningful estimates for the aircraft parameters.

Mehra, R. K.↗
NASA NTRS1985

An eigensystem realization algorithm for modal parameter identification and model reduction

A method called the eigensystem realization algorithm is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular-value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system and noise modes. For illustration of the algorithm, an example is shown using experimental data from the Galileo spacecraft.

Juang, J.-N.↗
NASA NTRS1985

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

Juang, J. N.↗
NASA NTRS1988

Parameter identification and sensitivity analysis for a robotic manipulator arm

The development of a nonlinear dynamic model for large oscillations of a robotic manipulator arm about a single joint is described. Optimization routines are formulated and implemented for the identification of electrical and physical parameters from dynamic data taken from an industrial robot arm. Special attention is given to difficulties caused by the large sensitivity of the model with respect to unknown parameters. Performance of the parameter identification algorithm is improved by choosing a control input that allows actuator emf to be included in an electro-mechanical model of the manipulator system.

Brewer, D. W.↗
NASA NTRS1981

On-line structural parameter identification

Algorithms are presented for on-line parameter identification of structural dynamic systems. As an example, they are used to calculate the parameters of a modal model of a flexible beam. The algorithms are tested using hardware consisting of a 12 ft. beam with four voice coil actuators and nine noncontacting displacement sensors. They are programmed in a CDC Cyber 175 digital computer which provides input command signals for the actuators, reads the sensor data, and processes the algorithm to calculate consistent estimates of the modal parameters of the beam. Experimental results are compared with those of simulation analysis.

Thau, F. E.↗
NASA NTRS1974

Application of a parameter identification technique to a hingeless helicopter rotor

A mathematical model of a gyro-controlled, three-bladed hingeless helicopter rotor was developed and parameters of the model were estimated using a parameter identification technique. The flapping and feathering degrees of freedom of the blades were modeled. The equations of the model contain time-varying, periodic coefficients due to the forward speed of the rotor. A digital simulation of the analytical model was compared with wind-tunnel measurements to establish the validity of the model. Comparisons of steady-state and transient solutions of the analytical model with the tunnel measurements gave reasonably good matching of gyro angle but less satisfactory matching of hub moment measurements. Further improvements were obtained by use of a parameter identification technique to adjust as many as 10 parameters of the analytical model. The sensitivity of the blade response to small changes in the parameters was also calculated.

Kanning, G.↗
NASA NTRS1986

The role of modeling and flight testing in rotorcraft parameter identification

The importance of recognizing that each lower-order model used for rotorcraft parameter identification has a limited range of applicability is illustrated in some detail. Examples are given to illustrate the use of conditioning the test input signals and the potential of using multi-axis test inputs to enhance the parameter identifiability. The paper discusses the benefits and limitations of using frequency sweeps as flight-test input signals for identification of frequency response for rotorcraft and for the subsequent fitting of parametric transfer-function models. This paper demonstrates the major role played by analytical modeling and the understanding of the physics involved in the rotorcraft flight dynamics, particularly understanding the limit of lower-order models, in achieving successful rotorcraft parameter identification.

Chen, R. T. N.↗
NASA NTRS1989

Parameter identification for an abstract Cauchy problem by quasilinearization

A parameter identification problem is considered in the context of a linear abstract Cauchy problem with a parameter-dependent evolution operator. Conditions are investigated under which the gradient of the state with respect to a parameter possesses smoothness properties which lead to local convergence of an estimation algorithm based on quasi-linearization. Numerical results are presented concerning estimation of unknown parameters in delay-differential equations.

Brewer, Dennis W.↗
NASA NTRS1993

Parameter identification for an abstract Cauchy problem by quasilinearization

A parameter identification problem is considered in the context of a linear abstract Cauchy problem with a parameter-dependent evolution operator. Conditions are investigated under which the gradient of the state with respect to a parameter possesses smoothness properties which lead to local convergence of an estimation algorithm based on quasi-linearization. Numerical results are presented concerning estimation of unknown parameters in delay-differential equations.

Brewer, Dennis W.↗
NASA NTRS1971

Parameter identification using a creeping-random-search algorithm

A creeping-random-search algorithm is applied to different types of problems in the field of parameter identification. The studies are intended to demonstrate that a random-search algorithm can be applied successfully to these various problems, which often cannot be handled by conventional deterministic methods, and, also, to introduce methods that speed convergence to an extremal of the problem under investigation. Six two-parameter identification problems with analytic solutions are solved, and two application problems are discussed in some detail. Results of the study show that a modified version of the basic creeping-random-search algorithm chosen does speed convergence in comparison with the unmodified version. The results also show that the algorithm can successfully solve problems that contain limits on state or control variables, inequality constraints (both independent and dependent, and linear and nonlinear), or stochastic models.

Parrish, R. V.↗
NASA NTRS1974

Nonlinear parameter identification: Ballistic range experience applicable to flight testing

The parameter identification scheme being used is a differential correction least squares procedure (Gauss-Newton method). The position, orientation, and derivatives of these quantities with respect to the parameters of interest (i.e., sensitivity coefficients) are determined by digital integration of the equations of motion and the parametric differential equations. The application of this technique to three vastly different sets of data is used to illustrate the versatility of the method and to indicate some of the problems that still remain.

Chapman, G.↗
NASA NTRS1985

Parameter identification in distributed systems

This paper describes a method for the identification of the parameters entering into the equations of motion of distributed systems. Because the motion of distributed systems is described in terms of partial differential equations, these parameters are in general continuous functions of the spatial variables. For vibrating systems, these parameters ordinarily represent the mass, stiffness and damping distributions. In this paper, these distributions are expanded in terms of finite series of known functions of the spatial variables multiplied by undetermined coefficients. It is assumed that the nature of the equations of motion is known and that a limited number of eigenvalues and eigenfunctions is identified in advance. Use is then made of the least squares method, in conjunction with the eigenfunctions' orthogonality, to compute the undetermined coefficients, thus identifying the system distributed parameters. A method for the identification of the eigensolution is also presented. The procedure for the identification of the eigensolution and of the system parameters is demonstrated via a numerical example.

Baruh, H.↗
NASA NTRS1987

Application of physical parameter identification to finite-element models

The time domain parameter identification method described previously is applied to TRW's Large Space Structure Truss Experiment. Only control sensors and actuators are employed in the test procedure. The fit of the linear structural model to the test data is improved by more than an order of magnitude using a physically reasonable parameter set. The electro-magnetic control actuators are found to contribute significant damping due to a combination of eddy current and back electro-motive force (EMF) effects. Uncertainties in both estimated physical parameters and modal behavior variables are given.

Bronowicki, Allen J.↗
NASA NTRS1978

Estimation of longitudinal aircraft characteristics using parameter identification techniques

This study compares the results from different parameter identification methods used to determine longitudinal aircraft characteristics from flight data. In general, these comparisons have found that the estimated short-period dynamics (natural frequency, damping, transfer functions) are only weakly affected by the type of identification method, however, the estimated aerodynamic coefficients may be strongly affected by the type of identification method. The estimated values for aerodynamic coefficients were found to depend upon the type of math model and type of test data used with each of the identification methods. The use of fairly complete math models and the use of long data lengths, combining both steady and nonsteady motion, are shown to provide aerodynamic coefficient values that compare favorably with the results from other testing methods such as steady-state flight and full-scale wind-tunnel experiments.

Wingrove, R. C.↗
NASA NTRS1988

On-orbit system parameter identification

Viewgraphs and discussion on on-orbit system parameter identification are included. Topics covered include: dynamic programming filter (DPF); cost function and estimator; frequency domain formulation structrual dynamic identification; and attributes of DPF.

Simonian, Stepan S.↗