• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2000.11a
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• pp.199-204
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• 2000

Generally, The systems are so complex that it not possible to obtain reasonable model using physical insight. Also a model based on physical insight contains a number of unknown parameters even if the structure is derived from physical laws. To solve these problems, the systems identification is described in this paper. So, AC servo motor system which has both open loop and closed loop is selected as an example for identification. A state-space model of AC servo motor system is identified through open loop experiment and identified through closed loop experiment and using pole placement integral controller to open loop system. As the results, From ARMA model, We have obtained continuous-time state space model.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.269-269
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• 2000

This paper presents a predictive control with H$_{\infty}$ suboptimal performance which is robust to disturbances and has a guaranteed stability. In order to derive the control law conveniently, state-space based approach, where the state variable is involved explicitly in the controller design and implementation is allowed. So an input-output model is converted to an equivalent observable canonical state-space form. The suggested control guarantees the norm bounded system output values from disturbances. A systematic way using the LMI method is presented to obtain appropriate parameters for Quadratic stability condition and optimization problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.107-114
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• 1995

The objective of this study is to design a one-stage dynamic prediction model with Kalman state space model. For a model verification, it is compared with EWMA(Exponentially Weighed Moving Average) model. The model designed in this research can be extended to process prevention control and quality monitoring.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.925-932
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• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.444-448
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• 2003

In this paper, the Lagrange dynamics is studied. A state space representation of Lagrange dynamics and control algorithm based on the state feedback pole placement are presented. The state space model presented is descriptor type linear parameter dependent system. It is shown that the control algorithms based on the linear system theory can be applicable to the state space representation of Lagrange dynamics. To show that the linear system theory can be applicable to the state space representation of Lagrange dynamics, the LMI based regional pole-placement design algorithm is developed and present two examples.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1038-1046
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• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.111-116
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• 2006

Given a state, it is essential to for the finite state model analysis (such as model checking) to decide whether or not the state is reachable. W a site of the model is small, the whole state space is to be explored exhaustively. However, it is very difficult or even impossible if a size of the model is large. In this case, the model can be reduced into a smaller one via abstraction which does not allow e false positive error. this paper, we devise such an abstraction and apply it to the Nemorize game solving. As a result, unsolved game due to the state explosion problem is solved with the proposed abstraction.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2340-2344
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• 2003

This paper considers a tracking filter algorithm which can track a maneuvering target. Multiresolutional Interacting Multiple Model (MRIMM) algorithm is proposed to reduce computational burden. In this paper multiresolutional state space model equation and multiresolutional measurement equation are derived by using wavelet transform. This paper shows the outline of MRIMM algorithm. Simulation results show that MRIMM algorithm maintains a good tracking performance and reduces computational burden.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.2 s.173
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• pp.290-302
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• 2000

In this paper, we study on the identification of discrete-time state-space model for robust control of tandem cold mill, using a MOESP(MIMO output-error state-space model identification) algorithm based on subspace method. It is shown that the identified model is well adapted to input-output data sets, which are obtained from nonlinear mathematical equations of tandem cold mill. Furthermore, deterministic H$\infty$ norm bounds on uncertainties including modeling errors and disturbances are quantitatively identified in the frequency domain. Finally, the results give a basic idea to determine weighting functions included in formulating some robust control problems of tandem cold mill.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.485-492
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• 1998

Most forecasting models often fail to produce appropriate forecasts because we build a model based on the assumption of the data being generated from the only one stochastic process. However, in many real problems, the time series data are generated from one stochastic process for a while and then abruptly undergo certain structural changes. In this paper, we assume the basic underlying process is the simple state-space model with random level and deterministic drift but interrupted by three types of exogenous shocks: level shift, drift change, outlier. A Bayesian procedure to detect, estimate and adapt to the structural changes is developed and compared with simple, double and adaptive exponential smoothing using simulated data and the U.S. leading composite index.