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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters

The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some sample results are compared to data obtained from testing hardware inverters.

Brush, Andrew S.↗
NASA NTRS1989

Automated frequency domain system identification of a large space structure

This paper presents the development and experimental results of an automated on-orbit system identification method for large flexible spacecraft that yields estimated quantities to support on-line design and tuning of robust high performance control systems. The procedure consists of applying an input to the plant, obtaining an output, and then conducting nonparametric identification to yield the spectral estimate of the system transfer function. A parametric model is determined by curve fitting the spectral estimate to a rational transfer function. The identification method has been demonstrated experimentally on the Large Spacecraft Control Laboratory in JPL.

Yam, Y.↗
NASA NTRS1992

Autonomous Frequency-Domain Indentification

Test and data-processing system determines plant models and uncertainties. Integrated system of methods, digital signal-processing, and algorithms identifies parametric model of large, flexible structures. Experiments in development of system conducted on laboratory model intended to represent large space antenna or flexible spacecraft. Also applicable to many terrestrial systems, robust control of dynamic plants and processes, robust control of systems about which knowledge uncertain or incomplete, decentralized control, and knowledge-based control systems.

Mettler, Edward↗
NASA NTRS1999

Uncertainty Modeling Via Frequency Domain Model Validation

Abstract The majority of literature on robust control assumes that a design model is available and that the uncertainty model bounds the actual variations about the nominal model. However, methods for generating accurate design models have not received as much attention in the literature. The influence of the level of accuracy of the uncertainty model on closed loop performance has received even less attention. The research reported herein is an initial step in applying and extending the concept of model validation to the problem of obtaining practical uncertainty models for robust control analysis and design applications. An extension of model validation called 'sequential validation' is presented and applied to a simple spring-mass-damper system to establish the feasibility of the approach and demonstrate the benefits of the new developments.

Waszak, Martin R.↗
NASA NTRSDate not supplied

Frequency Domain Quasi-Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

Background and Motivation - Low Order Equivalent System (LOES) - From handling qualities analysis - Traditionally simplifying complex control law and plant - More easily understood form - Extend LOES to a complex model due to aeroelasticity - Maximum likelihood (Filter Error) System Identification - Z = H_loes (U + W) + V - There are a lot of parameters - System identification usually simplifies by assuming a value - Output Error and Equation Error - Results in biased estimates of the parameters - We are proposing a new method to solving this problem

Jeffrey Ouellette↗
NASA NTRSDate not supplied

Frequency Domain Quasi Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

Background and Motivation - Low Order Equivalent System (LOES) - From handling qualities analysis - Traditionally simplifying complex control law and plant - More easily understood form - Extend LOES to a complex model due to aeroelasticity - Maximum likelihood (Filter Error) System Identification - Z = H_loes (U + W) + V - There are a lot of parameters - Estimate noise parameters - Extra outputs mean extra parameters to estimate - Usually assume noise model to simplify - Output Error and Equation Error - Results in biased estimates of the parameters - Sensing flexible aircraft have many outputs - Quasi maximum likelihood exploits redundancy of outputs

Jeffrey Ouellette↗