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Friday, May 8, 2020 | History

2 edition of Adaptive identification and control of structural dynamics using recursive lattice filters found in the catalog.

Adaptive identification and control of structural dynamics using recursive lattice filters

N Sundararajan

Adaptive identification and control of structural dynamics using recursive lattice filters

by N Sundararajan

  • 40 Want to read
  • 36 Currently reading

Published by National Aeronautics and Space Administration, Scientific and Technical Information Branch, For sale by the National Technical Information Service] in Washington, D.C, [Springfield, Va .
Written in English

    Subjects:
  • Adaptive control systems,
  • Structural dynamics,
  • Vibration

  • Edition Notes

    StatementN. Sundararajan, Raymond C. Montgomery, and Jeffrey P. Williams
    SeriesNASA technical paper -- 2371
    ContributionsMontgomery, Raymond C, Williams, Jeffrey P, United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch, Langley Research Center
    The Physical Object
    Paginationiii, 47 p. :
    Number of Pages47
    ID Numbers
    Open LibraryOL14933649M

    An adapative algorithm is used to estimate a time varying signal. There are many adaptive algorithms such as Recursive Least Square (RLS) and Kalman filters, but the most commonly used is the Least Mean Square (LMS) algorithm. It is a simple but powerful algorithm that can be implemented to take advantage of Lattice FPGA architectures. Apply adaptive filters to signal separation using a structure called an adaptive line enhancer (ALE). In adaptive line enhancement, a measured signal x(n) contains two signals, an unknown signal of interest v(n), and a nearly-periodic noise signal eta(n)ProjectionFilter: Compute output, error and coefficients using affine projection (AP), Algorithm.

    STAR recursive least square lattice adaptive filters Abstract: The recursive least square lattice (LSL) algorithm based on the newly developed scaled tangent rotations (STAR) is derived. Similar to other recursive least square lattice algorithms for adaptive filtering, this Cited by: 5. Specify the reflection process step size of the gradient adaptive lattice filter as a scalar numeric value between 0 and 1, both inclusive. The default value is the StepSize property value. Tunable: Yes. Dependencies. Use this property only if the Method property is set to 'Gradient Adaptive Lattice'.reset: Reset internal states of System, object.

    Lecture 8 2 Lattice Predictors † Order -Update Recursions for Prediction errors Since the predictors obey the recursive{in{order equations am = 2 4 am¡1 0 3 5 +¡m 2 4 0 aB m¡1 3 5 aB m = 2 4 0 aB m¡1 3 5 +¡m 2 4 am¡1 0 3 5 it is natural that prediction errors can be expressed in recursive{in{order Size: KB. An adaptive lattice algorithm for recursive filters.


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Adaptive identification and control of structural dynamics using recursive lattice filters by N Sundararajan Download PDF EPUB FB2

The adaptive identification and control using the lattice filters proposed previously are illustrated first by simulations for a free-free beam and then for a more complex two-dimensional free-free grid structure.

Adaptive identification and control of structural dynamics systems using recursive lattice filters Author: N Sundararajan ; Raymond C Montgomery ; Jeffrey P Williams ; United States. Adaptive Control of a Flexible Beam Using Least Square Lattice Filters IEEE Transactions on Aerospace and Electronic Systems, Vol.

AES, No. 5 Parameter estimation of spacecraft structural dynamics from flight test dataCited by: Lattice filters have been used primarily in speech and signal processing, but they have utility in adaptive control because of their order-recursive nature.

They are especially useful in dealing with structural dynamics systems wherein the order of a controller required to damp a vibration is variable depending on the number of modes significantly : N.

Sundararajan, Raymond C. Montgomery. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged.

An integrated lattice filter adaptive control system is developed for the control of time-varying CMM structural vibrations. An efficient algorithm is developed to provide a link between the adaptive lattice filter and the minimum variance control by directly utilizing the lattice filter parameters at time t − 1 for by: 2.

The use of recursive lattice filters for identification and adaptive control of large space structures was studied. Lattice filters are used widely in the areas of speech and signal processing.

Herein, they are used to identify the structural dynamics model of the flexible structures. This identified model is then used for adaptive control. The developed algorithm uses directly the lattice filter parameters to track and control the CMM structural dynamics and is implemented on a Sheffield horizontal arm Coordinate Measuring Machine using a floating point Digital Signal Processor TMSCCited by: 1.

Adaptive identification and control of structural dynamics using recursive lattice filters [microform] User manual for BUNVIS-RG [microform]: an exact buckling and vibration program for lattice structures, Vibration, acoustic, and shock design and test criteria for.

Decoupling The Structural Mode Estimated Using Recursive Lattice Filters, Proceedings of the 21st IEEE Conference on Decision and Control, pp Sundararaj an, N., and Montgomery, R.

Identification of Structural Dynamics System Using Least Square Lattice Filters. Journal of Guidance, Control and Dynamics, i, pp. Author: N. Sundararajan. A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented.

Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. In this paper an alternative approach using adaptive lattice filters (ALF) for the on-line identification of the transfer function of a single flexible link with shifting payloads is presented.

The link's dynamics are altered through the introduction of various payloads, which result in a shift of its natural by: 3. The adaptive identification and control using the lattice filters proposed previously are illustrated first yT = [yl(i), y2(i),yNS(i)] by simulations for a free-free beam and then for a more where the superscript NS represents the number of.

The choice of filter structure to adapt, algorithm design and the approximation properties for each type of algorithm are also addressed. This work recasts the theory of adaptive IIR filters by concentrating on recursive lattice filters, freeing systems from the need for direct-form filters.;A solutions manual is available for instructors by: An application of recursive covariance lattice algorithms to the adaptive estimation and control of a manipulator with one flexible link is presented.

These algorithms are a set of pure order recursive lattice equations, which can in principle identify the effective order and the corresponding parameters of an ARMA prediction model of the by: 4.

The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures.

This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the Author: G. Goglia and N.

Sundararajan. Nonlinear structural dynamical system identification using adaptive particle filters Article in Journal of Sound and Vibration () October with Reads How we measure 'reads'. EKF has been one of the most widely used tools for joint input/parameter identification in structural dynamics, and it has been adopted in many applications, such as damage identification [10, symmetric transversal form, and the lattice form are the ones often employed in adaptive filtering applications.

Applications Of Adaptive Filters Adaptive filters are widely used in telecommunications, control systems, radar systems, and in other systems where minimal information is available about the incoming Size: KB.

trol is attempted. A summary of the experimental results obtained using lattice filters is described in reference 3. An adaptive control scheme using lattice filter identification and modal description has been developed in reference 4.

Alternate schemes of using input-output models instead of modal form from lattice filters is described therein. An adaptive filter using Darwinian algorithm for system identification and control.

In Proceedings of the IEEE International Conference on Systems, Man and Cited by: Adaptive Modeling, Identification, and Control of Dynamic Structural Systems.

II: Applications In this paper, five examples for the application of adaptive modeling, identification, and control techniques in structural dynamics are presented. ASCE Subject Headings: Structural models, Structural control, Structural dynamics, Adaptive.Common Applications System Identification –– Using an Adaptive Filter to Identify an Unknown System.

One common adaptive filter application is to use adaptive filters to identify an unknown system, such as the response of an unknown communications channel or the frequency response of an auditorium, to pick fairly divergent applications.