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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Performance comparison of five frequency domain system identification techniques for helicopter higher harmonic control

This paper presents the results of a computer simulation comparing the performance of five system identification techniques currently proposed for use with helicopter, frequency domain, higher harmonic vibration control algorithms. The system identification techniques studied were: (1) the weighted least squares method in moving block format, (2) the classical Kalman filter, (3) a generalized Kalman filter, (4) the classical least mean square (LMS) filter, and (5) a generalized LMS filter. The generalized Kalman and LMS filters were derived by allowing for multistep operation, rather than the single-step update approach used by their classical versions. Both open-loop and closed-loop (vibration control mode) identification results are presented in the paper. The algorithms are evaluated in terms of their accuracy, stability, convergence properties, computation speeds, and the relative ease with which these techniques may be directly applied to the helicopter vibration control problem.

Jacklin, Stephen A.↗
NASA NTRS1988

A comparative overview of modal testing and system identification for control of structures

A comparative overview is presented of the disciplines of modal testing used in structural engineering and system identification used in control theory. A list of representative references from both areas is given, and the basic methods are described briefly. Recent progress on the interaction of modal testing and control disciplines is discussed. It is concluded that combined efforts of researchers in both disciplines are required for unification of modal testing and system identification methods for control of flexible structures.

Juang, J.-N.↗
NASA NTRS1986

System identification techniques for helicopter higher harmonic control

This paper presents and compares several system identification techniques proposed for use with higher harmonic control algorithms designed to alleviate helicopter vibration. All method for actively controlling helicopter vibration require the knowledge of how the vibration outputs are related to the control inputs. Off-line or batch identification methods for obtaining this knowledge are presented first. Then the more advanced, adaptive identification techniques proposed to track the helicopter model parameters in flight are discussed. Considerations regarding system identifiability, identification algorithm stability, and computer implementation are also discussed.

Jacklin, S. A.↗
NASA NTRS1987

A comparative overview of modal testing and system identification for control of structures

This paper presents a comparative overview of the disciplines of modal testing used in structural engineering and system identification used in control theory. A list of representative references from both areas is given and the basic methods are briefly described. Recent progress on the interaction of modal testing and control disciplines is discussed. It is concluded that combined efforts of researchers in both disciplines are required for unification of modal testing and system identification methods for control of flexible structures.

Juang, Jer-Nan↗
NASA NTRS2003

System Identification of Mistuned Bladed Disks from Traveling Wave Response Measurements

A new approach to modal analysis is presented. By applying this technique to bladed disk system identification methods, one can determine the mistuning in a rotor based on its response to a traveling wave excitation. This allows system identification to be performed under rotating conditions, and thus expands the applicability of existing mistuning identification techniques from integrally bladed rotors to conventional bladed disks.

Feiner, D. M.↗
NASA NTRS1974

Computer experiments in preparation of system identification from transient rotor model tests, part 2

System identification methods which can extract model rotor paramenters with reasonable accuracy from noise polluted blade flapping transient measurements were developed. Usually parameter identification requires data on the state variables, that is on deflections and on rate of deflections. The small size of rotor models makes it, however, difficult to measure more than the blade flapping deflections. For the computer experiments it was, therefore, assumed that only noisy deflection measurements are available. Parameter identifications were performed for one and two unknown parameters. Both rotating coordinates and multiblade coordinates were used. It was found that data processing with a digital filter allowed by numerical differentiation a sufficiently accurate determination of the rates of deflection and of the accelerations to obtain reasonable parameter estimates with a simple linear estimator.

Hohenemser, K. H.↗
NASA NTRS1993

System identification and model reduction using modulating function techniques

Weighted least squares (WLS) and adaptive weighted least squares (AWLS) algorithms are initiated for continuous-time system identification using Fourier type modulating function techniques. Two stochastic signal models are examined using the mean square properties of the stochastic calculus: an equation error signal model with white noise residuals, and a more realistic white measurement noise signal model. The covariance matrices in each model are shown to be banded and sparse, and a joint likelihood cost function is developed which links the real and imaginary parts of the modulated quantities. The superior performance of above algorithms is demonstrated by comparing them with the LS/MFT and popular predicting error method (PEM) through 200 Monte Carlo simulations. A model reduction problem is formulated with the AWLS/MFT algorithm, and comparisons are made via six examples with a variety of model reduction techniques, including the well-known balanced realization method. Here the AWLS/MFT algorithm manifests higher accuracy in almost all cases, and exhibits its unique flexibility and versatility. Armed with this model reduction, the AWLS/MFT algorithm is extended into MIMO transfer function system identification problems. The impact due to the discrepancy in bandwidths and gains among subsystem is explored through five examples. Finally, as a comprehensive application, the stability derivatives of the longitudinal and lateral dynamics of an F-18 aircraft are identified using physical flight data provided by NASA. A pole-constrained SIMO and MIMO AWLS/MFT algorithm is devised and analyzed. Monte Carlo simulations illustrate its high-noise rejecting properties. Utilizing the flight data, comparisons among different MFT algorithms are tabulated and the AWLS is found to be strongly favored in almost all facets.

Shen, Yan↗
NASA NTRS1992

A second-order structural identification procedure via state space-based system identification

A theory is presented for transforming the system theory-based realization models into the corresponding physical coordinate-based structural models. The theory has been implemented into a computational procedure and applied to several example problems. The results show that the present transformation theory yields an objective model basis possessing a unique set of structural parameters from an infinite set of equivalent system realization models. For proportionally damped systems, the transformation directly and systematically yields the normal modes and modal damping. When non-proportional damping is present, the relative magnitude and phase of the damped model shapes are separately characterized, and a corrective transformation is then employed to capture the undamped normal modes and non-diagonal modal damping matrix.

Alvin, K. F.↗
NASA NTRS1985

On-orbit systems identification of flexible spacecraft

The process of on-orbit systems identification of flexible spacecraft is examined in terms of the difficulties that are expected because of the potentially unmanageable number of unknown model parameters due to the very high order system models involved. A Jordon block canonical form and global model parameters are used to reduce the number of unknown parameters to manageable numbers. A Bayesian approach is discussed which enables the merging of theoretical models with ground or on-orbit test results by using Unconditional Maximum Likelihood Parameter Estimation. A Modified Newton-Raphson technique is proposed for determining the model parameter estimates and an expected fit error criterion is recommended for the determination of the model structure and order reduction.

Taylor, L. W., Jr.↗
NASA NTRSDate not supplied

Recent System Identification Research at NASA Langley Research Center

This talk summarizes some of the recent advances in system identification at NASA Langley Research Center. Efforts discussed were applied to aeroelastic models, aircraft with redundant inputs and feedback control active, and aircraft flying in turbulence. Topics mentioned include experiment design with orthogonal multisine inputs, frequency response estimation, maximum likelihood parameter estimation, and parameter estimation considering process noise.

NASA LaRC↗
NASA NTRS1992

A Gamma Memory Neural Network for System Identification

A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

Motter, Mark A.↗
NASA NTRSDate not supplied

System Identification for Propellers at High Incidence Angles

Propellers used for electric vertical takeoff and landing (eVTOL) aircraft propulsion systems experience a wide range of aerodynamic conditions, including large incidence angles relative to oncoming airflow. In oblique flow, propellers exhibit deviations in thrust and torque oriented along the propeller axis of rotation, as well as significant off-axis forces and moments. Although important for modeling eVTOL aircraft aerodynamics, sparse experimental data or mathematical models exist for propellers at incidence. This paper describes a propulsion system modeling methodology for the Langley Aerodrome No. 8 (LA-8) tandem tilt-wing, eVTOL aircraft. System identification methods are applied to isolated propeller wind tunnel data gathered across the vehicle's flight envelope to develop a mathematical model of the propulsion system, including a static motor model, dynamic motor model, and propeller aerodynamic model. Modeling results validated against data withheld from the modeling process indicate good predictive capability and agree with theoretical expectations. The results are followed by a discussion of model implementation strategies into high-fidelity eVTOL aircraft simulations.

system identification↗
NASA NTRS2021

System Identification for Propellers at High Incidence Angles

Propellers used for electric vertical takeoff and landing (eVTOL) aircraft propulsion systems experience a wide range of aerodynamic conditions, including large incidence angles relative to oncoming airflow. In oblique flow, propellers exhibit deviations in thrust and torque oriented along the propeller axis of rotation, as well as significant off-axis forces and moments. Although important for modeling eVTOL aircraft aerodynamics, sparse experimental data or mathematical models exist for propellers at incidence. This paper describes a propulsion system modeling methodology for the LA-8 tandem tilt-wing, eVTOL aircraft. System identification methods are applied to isolated propeller wind tunnel data gathered across the vehicle's flight envelope to develop a mathematical model of the propulsion system, including a static motor model, dynamic motor model, and propeller aerodynamic model. Modeling results validated against data withheld from the modeling process indicate good predictive capability and agree with theoretical expectations. The results are followed by a discussion of model implementation strategies into high-fidelity eVTOL aircraft simulations.

system identification↗
DOE OSTI2020

Bayesian differential programming for robust systems identification under uncertainty

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling. This allows an efficient inference of the posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods, including nonlinear oscillators, predator–prey systems and examples from systems biology. Taken together, our findings put forth a flexible and robust workflow for data-driven model discovery under uncertainty. All codes and data accompanying this article are available at https://bit.ly/34FOJMj .

Science & Technology - Other Topics↗