Time Domain State Space Identification of Structural Systems

1995 ◽  
Vol 117 (4) ◽  
pp. 608-618 ◽  
Author(s):  
Ketao Liu ◽  
David W. Miller
Keyword(s):  
State Space ◽  
Time Domain ◽  
Control Experiment ◽  
Model Updating ◽  
Least Square ◽  
Structural Systems ◽  
Identification Algorithm ◽  
Range Space ◽  
Domain State ◽  
Identification Technique

An integrated time domain state space identification technique for structural systems is presented. This technique integrates the Observability Range Space Extraction identification algorithm, Balanced Realization model reduction algorithm, and Least Square model updating algorithm to generate low order and highly accurate state space models for structural systems based upon time domain data. The algorithms are integrated in such a manner that the Observability Range Space Extraction identification algorithm is used to generate an initial overparameterized state space model and then the Balanced Realization model reduction and Least Square model updating algorithms are used to iteratively reduce and update the model to achieve minimum prediction errors in time domain. We shall present the Observability Range Space Extraction identification algorithm and the Least Square model updating algorithm and discuss the integrated identification technique. The MIT Middeck Active Control Experiment (MACE) is used as an application example. MACE is an active structure control experiment to be conducted in the Space Shuttle middeck. Results of ground experiments using this technique will be discussed.

Experimental Identification Technique of Nonlinear Beams in Time Domain

1997 ◽  
Author(s):  
Kimihio Yasuda ◽  
Keisuke Kamiya
Keyword(s):  
Time Domain ◽  
Harmonic Balance ◽  
Least Square Method ◽  
Least Square ◽  
Governing Equations ◽  
Experimental Identification ◽  
Initial Values ◽  
Nonlinear Beams ◽  
Identification Technique ◽  
The Time Domain

Abstract In previous papers the authors proposed a new experimental identification technique applicable to elastic structures. The proposed technique is based on the principle of harmonic balance, and can be classified as the frequency domain technique. The technique requires the excitation force to be periodic. This is in some cases a restriction. So another technique free from this restriction is of use. In this paper, as a first step for developing such techniques, a technique applicable to beams is proposed. The proposed technique can be classified as the time domain one. Two variations of the technique are proposed, depending on what methods are used for estimating the parameters of the governing equations. The first method is based on the usual least square method. The second is based on solving a minimization problem with constraints. The latter usually yields better results. But in this method, an iteration procedure is used, which requires initial values for the parameters. To determine the initial values, the first method can be used. So both methods are useful. Finally the applicability of the proposed technique is confirmed by numerical simulation and experiments.

Synthetic seismograms using the state‐space approach

Geophysics ◽  
1979 ◽  
Vol 44 (5) ◽  
pp. 880-895 ◽  
Author(s):  
J. M. Mendel ◽  
N. E. Nahi ◽  
M. Chan
Keyword(s):  
State Space ◽  
Time Domain ◽  
State Space Model ◽  
Layered Media ◽  
State Space Models ◽  
State Equations ◽  
Space Model ◽  
Domain State ◽  
The Time Domain ◽  
Treat All

We develop time‐domain state‐space models for lossless layered media which are described by the wave equation and boundary conditions. We develop state‐space models for two cases: (1) source and sensor at the surface, and (2) source and sensor in the first layer. Our models are for nonequal one‐way traveltimes; hence, they are more general than most existing models of layered media which are usually for layers of equal one‐way traveltimes. A notable exception to this is the work of Wuenschel (1960); however, most of the useful results even in his paper are developed only for the uniform traveltime case. Our state‐space model treat all of the equations that describe a layered‐media system together in the time domain. Earlier approaches (e.g., Wuenschel, 1960; Robinson, 1968) recursively connect adjacent layers by means of frequency‐domain relationships. We refer to our state equations as “causal functional equations.” They actually represent a new class of equations. Why are we interested in a different class of models for what appears to be a well‐studied system? As is well known, there is a vast literature associated with systems which are described by time‐domain state‐space models. Most recent results in estimation and identification theories, for example, require a state‐space model. These time‐domain techniques have proven very beneficial outside of the geophysics field and we feel should also be beneficial in the geophysics field. In fact, our ultimate objective is to apply those theories to the layered‐media problem; but, to do so, of course, requires state‐space models—hence, this paper.

Frequency Domain Structural System Identification by Observability Range Space Extraction

1996 ◽  
Vol 118 (2) ◽  
pp. 211-220 ◽  
Author(s):  
Ketao Liu ◽  
Robert N. Jacques ◽  
David W. Miller
Keyword(s):  
System Identification ◽  
Frequency Domain ◽  
Structural Control ◽  
Control Experiment ◽  
Space Shuttle ◽  
State Space Model ◽  
Identification Algorithm ◽  
Structural System ◽  
Range Space ◽  
Structural System Identification

This paper presents the Frequency Domain Observability Range Space Extraction (FORSE) identification algorithm. FORSE is a singular value decomposition based identification algorithm which constructs a state space model directly from frequency domain data. The concept of system identification by observability range space extraction was developed by generalizing the Q-Markov Covariance Equivalent Realization and Eigensystem Realization Algorithm. The numerical properties of FORSE are well behaved when applied to multi-variable and high dimensional structural systems. It can achieve high modeling accuracy by properly overparameterizing the system. The effectiveness of this algorithm for structural system identification is demonstrated using the MIT Middeck Active Control Experiment (MACE). MACE is an active structural control experiment to be conducted in the Space Shuttle middeck. Results of ground experiments using this algorithm will be discussed.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

A Frequency Domain Versus a Time Domain Identification Technique for Nonlinear Parameters Applied to Wire Rope Isolators

2000 ◽  
Vol 123 (4) ◽  
pp. 645-650 ◽  
Author(s):  
Gaetan Kerschen ◽  
Vincent Lenaerts ◽  
Stefano Marchesiello ◽  
Alessandro Fasana
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Nonlinear Dynamical Systems ◽  
Wire Rope ◽  
Restoring Force ◽  
Nonlinear Parameters ◽  
Nonlinear Dynamical ◽  
Domain Identification ◽  
Identification Technique ◽  
Reverse Path Method

The present paper aims to compare two techniques for identification of nonlinear dynamical systems. The Conditioned Reverse Path method, which is a frequency domain technique, is considered together with the Restoring Force Surface method, a time domain technique. Both methods are applied for experimental identification of wire rope isolators and the results are compared. Finally, drawbacks and advantages of each technique are underlined.

Time Domain Hydroelastic Analysis of a Flexible Marine Structure Using State-Space Models

2007 ◽  
Author(s):  
Reza Taghipour ◽  
Tristan Perez ◽  
Torgeir Moan
Keyword(s):  
Mathematical Model ◽  
State Space ◽  
Time Domain ◽  
State Space Model ◽  
Realization Theory ◽  
Regular Waves ◽  
Alternative State ◽  
Marine Structure ◽  
Hydroelastic Analysis ◽  
The Mathematical Model

This article deals with time-domain hydroelastic analysis of a marine structure. The convolution terms in the mathematical model are replaced by their alternative state-space representations whose parameters are obtained by using the realization theory. The mathematical model is validated by comparison to experimental results of a very flexible barge. Two types of time-domain simulations are performed: dynamic response of the initially inert structure to incident regular waves and transient response of the structure after it is released from a displaced condition in still water. The accuracy and the efficiency of the simulations based on the state-space model representations are compared to those that integrate the convolutions.

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