• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.380-380
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• 2000

We have developed a design/analysis tool for use with Mat lab whick is named as "Parametric Robust Control and Identification(PROCI)". The tool is composed of three parts: Part i) consists of the identification of the continuous time transfer function by using either time domain input-output data or frequency response data, which might be experimentally obtained. Part ii) is the CDM synthesis of classical controller such as PID, Lead/Lag compensators. In part iii), the analysis of robustness of overall system can be dealt with. This tool allows us to analyze completely most of robustness issues with respect to the interval uncertaintyncertainty

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.317-320
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• 2017

This paper examines approaches to the development of algorithms of parametric identification of models of complex systems from discrete observations. A modification of a known algorithm Kaczmarz which is designed for closed systems with perturbations, based on the methods of random search and investigates their statistical properties.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.556-562
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• 2002

This paper studies the identification of a combustion model, which is used to design a linear controller of a steam generation quantity and harmful exhaust gases of a Refuse Incineration Plant(RIP). Even though the RIP has strong nonlinearities and complexities, it is identified as a MIMO parametric ARX model from experimental input-output data sets. Unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. It is shown that the identified model well approximates the input-output combustion characteristics.

• Advances in aircraft and spacecraft science
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• v.11 no.2
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• pp.105-128
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• 2024

Vibrations in aerodynamic systems can lead to significant structural and performance issues. This paper presents a novel theorem for actively controlling aerodynamic vibrations through parametric stability analysis. The proposed approach models the aerodynamic system as a dynamic system with parametric excitation, allowing for the identification of stable and unstable regions in the parameter space. By strategically adjusting the system parameters, the vibrations can be effectively suppressed, enhancing the overall reliability and performance of the aerodynamic system. The theoretical underpinnings of the theorem are discussed, and the effectiveness of the approach is demonstrated through numerical simulations and experimental validation. The results show the potential of this method for practical implementation in various aerodynamic applications, such as aerospace engineering and wind turbine design.

• Smart Structures and Systems
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• v.19 no.3
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• pp.259-268
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• 2017

System identification of structures is one of the important aspects of structural health monitoring. The accuracy and efficiency of identification results is affected severely by measurement noises, especially when the structure system is large, such as bridge structures, and when online system identification is required. In this paper, the least square estimation (LSE) method is used combined with the substructure approach for identifying structural parameters of a cable-stay bridge with large degree of freedoms online. Numerical analysis is carried out by first dividing the bridge structure into smaller substructures and then estimates the parameters of each substructure online using LSE method. Simulation results demonstrate that the proposed approach is capable of identifying structural parameters, however, the accuracy and efficiency of identification results depend highly on the noise sensitivities of loading region, loading pattern as well as element size.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.462-470
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• 2003

This paper presents an algorithm of identifying parametric uncertainty by way of an interval model. For a given set of frequency response data from an uncertain linear SISO system of which the upper and the lower bounds of both magnitude and phase responses are represented, the proposed algorithm consists of two main parts: first, the nominal model is identified by using Least Square Estimation (LSE), and then an interval model is constructed by expanding the extremal properties of interval systems, so that tightly enclose the given envelopes within those of interval model. Two numerical examples are given to demonstrate and verify the developed algorithm. The identified interval model can be used for evaluating the worst case performance and stability margins against parametric uncertainty by using some extremal properties on interval systems.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.125-128
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• 1999

A linear time-invariant model can be described either by a parametric model or by a nonparametric model. Nonparametric models, for which a priori information is not necessary, are basically the response of the dynamic system such as impulse response model and frequency models. Parametric models, such as transfer function models, can be easily described by a small number of parameters. In this paper aiming to take benefit from both types of models, we will use linear-combination of basis fuctions in an impulse response using a few parameters. We will expand and generalize the Kautz functions as basis functions for dynamical system representations and we will consider estimation problem of transfer functions using Kautz function. And so we will present the influences of poles settings of Kautz function on the identification accuracy.

• Journal of KSNVE
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• v.10 no.1
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• pp.144-152
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• 2000

The identification of a nonlinear vibration system based on the time domain parametric model has been widely studied in recent years. In most of the studies, the NARMAX model has been used for the identification of a nonlinear system. However, the computational load for the identification with this model is quite heavy. In this paper, a new modeling procedure for nonlinear system identification in discrete time domain is proposed. The proposed model has less initial nonlinear terms than NARMAX model, and the terms in the proposed model are derived from physically meaningful way. The performance of the proposed method is evaluated through the simulation, and the result shows that the proposed model can identify the nonlinear characteristics of the vibration system very will less computational effort.

• Korean Journal of Computational Design and Engineering
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• v.11 no.3
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• pp.164-171
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• 2006

As collaborative design and configuration design are of increasing importance in product development, it becomes essential to exchange the feature and parametric CAD models among participants. A history-based parametric method has been proposed and implemented. But each translator which exchanges the feature and parametric information tends to be heavy because to implement duplicated functions such as the identification of the selected geometries, mapping between features which have different attributes. Furthermore. because the history-based parametric translator uses the procedural model as the neutral format, which is the XML macro file, the history-based parametric translators need a geometric modeling kernel to generate an internal explicit geometric model. To ease the problem, we implemented a shared integration platform, the TransCAD. The TransCAD separates translators from the XML macro files. The translators for various CAD systems need to communicate with only the TransCAD. To support the communication with the TransCAD, we exposed the functions of the TransCAD by using the Automation APIs, which is developed by Microsoft. The Automation APIs of the TransCAD consist of the part modeling functions, the data extraction functions, and the utility functions. Each translator uses these functions to translate a parametric CAD model from the sending CAD system into the XML format, or from the in format into the model of the receiving CAD system This paper introduces what the TransCAD is and how it works for the exchange of the feature and parametric models.