Autonomous frequency domain identification theory and experiment

Cover of: Autonomous frequency domain identification |

Published by National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, National Technical Information Service, distributor in Pasadena, Calif, [Springfield, Va .

Written in English

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Subjects:

  • Characterization.,
  • Control theory.,
  • Controllers.,
  • Matrices (Mathematics),
  • Models.,
  • Tuning.

Edition Notes

Book details

StatementY. Yam ... [et al.].
SeriesNASA contractor report -- NASA CR-184811.
ContributionsJet Propulsion Laboratory (U.S.)
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL15408690M

Download Autonomous frequency domain identification

A novel automated frequency domain identification methodology was developed and experimentally verified on the JPL/AFAL experiment facility which is designed for emu- lation of on-orbit testing and control scenarios. The testbed structure is shown in FigureCited by: 4.

Get this from a library. Autonomous frequency domain identification: theory and experiment. [Jet Propulsion Laboratory (U.S.);]. This particular book focuses on frequency domain approaches (versus time domain approaches) and is intended to be both comprehensive and foundational.

The book is well written with many diagrams, includes ample pictures and diagrams, and provides a decent companion to a stronger theoretical by:   They share research interests in system identification, signal processing, and measurement techniques.

They are the coauthors of a software package with a user-friendly graphical user interface called Frequency Domain System Identification Toolbox for Matlab(r), which covers the methods discussed in this book. The Autonomous Frequency Domain Identification program, AU-FREDI, is a system of methods, algorithms and software that was developed for the identification of structural dynamic parameters and system transfer function characterization for control of large space platforms and flexible : Y.

Yam. By this way, the autonomous identification of modal parameter for SISO, MIMO, OMA test can be realized, the best identification result can be obtained which only depends on the frequency bands of interest without relation to analysis methods and user.

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Description About Book System Identification – A Frequency Domain Approach, Second Edition From Amazon System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Y.

ROLAIN, J. SCHOUKENS, R. PINTELONOrder estimation for linear time-invariant systems using frequency domain identification methods IEEE Transactions on Automatic Control, 42 (), pp.

Google Scholar. In this study, modeling, identification and control aspects of an autonomous tractor have been investigated.

Three yaw dynamics models have been derived from the equations of motion of the system. The parameters of these transfer functions have been estimated through nonlinear least squares frequency domain identification. An Intuitive Introduction to Frequency Domain Identification Abstract: This chapter contains sections titled: Intuitive Approach.

The Errors-in-Variables Formulation. Generating Starting Values. Books > System Identification: A Freq > An Intuitive Introduction to Frequency.

Cite this paper as: Yin B., Lin X., Tang W., Jin Z. () Thruster Fault Identification for Autonomous Underwater Vehicle Based on Time-Domain Energy and Time-Frequency Entropy of Fusion Signal. Autonomous Frequency Domain Identification: Theory and Experiment.

Article. Apr ; This book is the first to address the problem of laminated composites containing stress concentrations in. Windowing in the Frequency Domain, (non causal) Filtering in Time Domain Time and Frequency Domain Identification: Differences Choice of the Model Unstable Plants Noise Models: Parametric or Non-parametric Noise Models Extended Frequency Range: Combination of Different Experiments The Errors-in-variables.

A new frequency-domain identification approach is first proposed to fit a parametric model (state space model) to approximate the convolution. This paper describes a practical system identification procedure for small, low-cost, fixed-wing unmanned aircraft.

Physical size and cost restrictions limit the sensing capabilities of these vehicles. The procedure is demonstrated on an Ultra Stick 25e, therefore emphasizing a minimum complexity approach compatible with a low-cost inertial sensor.

Marine Systems Identification, Modeling and Control is a concise, stand-alone resource covering the theory and practice of dynamic systems and control for marine engineering students and professionals.

Developed from a distance learning CPD course on marine control taught by the authors, the book presents the essentials of the subject, including system representation and. Chen, Y.-Y.; Lin, Y.-H. A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management.

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Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous.

Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory, techniques and algorithms used for the analysis and processing of non-stationary signals, as found in a wide range of applications including telecommunications, radar, and biomedical engineering.

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Modeling with vortex lattice method and frequency sweep flight test for a fixed-wing UAV Control Engineering Practice, Vol. 21, No. 12 System Identification for Small, Low-Cost, Fixed-Wing Unmanned Aircraft.

This paper presents a new frequency domain identification method for multi-input, multi-output (MIMO) systems. Based on experimentally determined frequency response function data, rational polynomial transfer function models of structural systems are identified.

1. Sensors (Basel). Oct 14;19(20). pii: E doi: /s A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management.

the frequency-domain decomposition (FDD) method in a wireless sensor network for automated mode shape extraction. In this study, a single-input multi-output (SIMO) subspace system identification strategy ideally suited for the decentralized computing architecture of a wireless monitoring system is pro-posed.

It is based on a weighted total least squares approach, starting from multiple input multiple output frequency response functions.

One of the specific advantages of the technique lies in the very stable identification of the system poles as a function of the specified system order, leading to easy-to-interpret stabilization diagrams.

Select Continuous-time or Discrete-time to specify whether the model is a continuous- or discrete-time transfer function. For discrete-time models, the number of poles and zeros refers to the roots of the numerator and denominator polynomials expressed in terms of the lag variable q^ (For discrete-time models only) Specify whether to estimate the model feedthrough.

"This book facilitates a sound understanding of the free-running oscillation mechanism, the start-up from the noise level, and the establishment of the steady-state oscillation.

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Downloaded: Abstract. This chapter deals with the control system development and flight test for an unconventional flight vehicle, namely, a tandem ducted-fan experimental flying platform. The first-principle modeling approach combined with the frequency system identification has been adopted to obtain a.

Frequency Domain Analysis Explained Predicting the future behavior of a process is key to the analysis of feedback control systems. Knowing how the controlled process will react to the controller's efforts allows the controller to choose the course of action required to drive the process variable towards the setpoint.

The true model as well as the disturbances affecting the system are assumed unknown. However, the physical parameters are assumed to enter the coefficients of the system transfer function multilinearly.

A set of models is identified by perturbing the physical parameters one-at-time and using a frequency domain identification technique. Brand new Book.

System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Used for prediction, control, physical interpretation, and the designing of any electrical systems, they are vital in the fields of electrical, mechanical, civil, and chemical engineering.

Autonomous Frequency-Domain Indentification. By Mark H. Milman, Edward Mettler, David S. Bayard, Fred Y. Hadaegh, Yeung Yam and Robert E. Scheid. Abstract. Test and data-processing system determines plant models and uncertainties.

Integrated system of methods, digital signal-processing, and algorithms identifies parametric model of large. Electrical Engineering System Identification A Frequency Domain Approach How does one model a linear dynamic system from noisy data.

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"A Frequency Domain Global Parameter Estimation Method for Multiple Reference Frequency Response Measurements", Mechanical System and Signal Processing, Vol.

2, No. 4,pp. You've reached the end of your free preview. Practical Aspects of the Frequency Domain Approach for Aircraft System Identification Practical aspects of the frequency-domain approach for aircraft system identification are explained and demonstrated.

Topics related to experiment design, flight data analysis, and dynamic modeling are included. With the proposed method, the high frequency noises in sensors can be eliminated greatly, and then high performance altitude information can be fused to provide support for SUAR in the autonomous landing process.

The effectiveness of the proposed method has been demonstrated by static tests, hovering tests and a series of autonomous landing tests. Robotics and Intelligent Systems A Virtual Reference Book Robert F. Stengel Princeton University Princeton, NJ Septem The Robotics and Intelligent Systems Virtual Reference Book is an assemblage of bookmarks for web pages that contain educational material.

The Table of Contents summarizes the Bookmarks Menu and provides links to each chapter. This particular book focuses on frequency domain approaches (versus time domain approaches) and is intended to be both comprehensive and foundational.

The book is well written with many diagrams, includes ample pictures and diagrams, and provides a decent companion to a stronger theoretical backing. Techniques for the efficient simulation of the most common autonomous regimes.

A presentation and comparison of the main stability-analysis methods in the frequency domain. A detailed examination of the instabilization mechanisms that delimit the operation bands of autonomous. The universe is a mathematical hologram. It’s made of ontological mathematics.

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