• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1411-1417
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• 2009

In this paper, we consider the design of $H_{\infty}$ control of linear discrete-time systems having no mathematical model. The basic approach is to use Q-learning which is a reinforcement learning method based on actor-critic structure. The model-free control design is to use not the mathematical model of the system but the informations on states and inputs. As a result, the derived iterative algorithm is expressed as linear matrix inequalities(LMI) of measured data from system states and inputs. It is shown that, for a sufficiently rich enough disturbance, this algorithm converges to the standard $H_{\infty}$ control solution obtained using the exact system model. A simple numerical example is given to show the usefulness of our result on practical application.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.83-87
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• 2004

In this paper, an electrical equivalent circuit is derived by solving system equations. A frequency characteristics graph is calculated and the characteristics curve of the linear Actuator Model System are simulated by the Matlab program The frequency characteristics of a linear actuator are analyzed on the structure of the linear Actuator Model System This paper proposed and analyzed the linear Actuator Model(LAM) by using Matlab program with linear actuator was verified computer simulation based on the energy conversion theory.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.506-514
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• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.1
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• pp.1-11
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• 2015

In order to perform a soil-isolation-structure interaction analysis of seismically isolated nuclear power plant (NPP) structures, the nonlinear behavior of a seismic isolation system may be converted to an equivalent linear model used in frequency domain analysis. Seismic responses for seismically isolated NPP containment structures subjected to a simple artificial acceleration history and different site class earthquakes are evaluated for the equivalent-linear and nonlinear models that have been applied to lead-rubber bearing (LRB) modeling. It can be observed that the maximum displacements of the equivalent linear model are larger than that of the nonlinear model. From the floor response spectrum analysis for the top of NPP containment structures, it can be observed that the spectral acceleration of an equivalent linear model at about 0.5 Hz frequency is about 2~3 times larger than that of a nonlinear model.

• Structural Engineering and Mechanics
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• v.18 no.1
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• pp.91-112
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• 2004

Concrete is a composite material and at meso-level, may be assumed to be composed of three phases: aggregate, mortar-matrix and aggregate-matrix interface. It is postulated herein that although non-linear material parameters are generally used to model this composite structure by finite element method, linear elastic fracture mechanics principles can be used for modelling at the meso level, if the properties of all three phases are known. For this reason, a novel meso-mechanical approach for concrete fracture which uses the composite material model with distributed-phase for elastic properties of phases and considers the size effect according to linear elastic fracture mechanics for strength properties of phases is presented in this paper. Consequently, the developed model needs two parameters such as compressive strength and maximum grain size of concrete. The model is applied to three most popular fracture mechanics approaches for concrete namely the two-parameter model, the effective crack model and the size effect model. It is concluded that the developed model well agrees with considered approaches.

• Journal of Ocean Engineering and Technology
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• v.32 no.1
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• pp.1-8
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• 2018

In order to analyze the response of the offshore structure numerically, the linear potential theory is generally applied for simplicity, and only the radiation damping is considered among various damping forces. Therefore, the results of a numerical simulation can be different from the motion of the structure in a real environment. To reduce the differences between the simulation results and experimental results, the viscous damping, which affects the motion of the structure, is also taken into account. The appropriate damping model is essential for the numerical simulation in order to obtain precise responses of the offshore structure. In this study, various damping models such as linear or quadratic damping and the nonlinear drag force from numerous slender bodies were used to simulate the free decay motion of the platform, and its characteristics were confirmed. The optimized damping model was found by comparing the simulation results to the experimental results. The hydrodynamic forces and wave exciting forces of the structure were obtained using WAMIT, and the free decay test was simulated using OrcaFlex. A free decay test of the scale model was performed by KRISO.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1169-1188
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• 2016

Real-time substructuring techniques are currently an advanced experimental method for testing large size specimens in the laboratory. In dynamic substructuring, the whole tested system is split into two linked parts, the part of particular interest or nonlinearity, which is tested physically, and the remanding part which is tested numerically. To achieve near-perfect synchronization of the interface response between the physical specimen and the numerical model, a good controller is needed to compensate for transfer system dynamics, nonlinearities, uncertainties and time-varying parameters within the physical substructures. This paper presents the substructuring approach and control performance of the linear and the adaptive controllers for testing the dynamic characteristics of soil-structure-interaction system (SSI). This is difficult to emulate as an entire system in the laboratory because of the size and power supply limitations of the experimental facilities. A modified linear substructuring controller (MLSC) is proposed to replace the linear substructuring controller (LSC).The MLSC doesn't require the accurate mathematical model of the physical structure that is required by the LSC. The effects of parameter identification errors of physical structure and the shaking table on the control performance of the MLSC are analysed. An adaptive controller was designed to compensate for the errors from the simplification of the physical model in the MLSC, and from parameter identification errors. Comparative simulation and experimental tests were then performed to evaluate the performance of the MLSC and the adaptive controller.

• Journal of Korean Port Research
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• v.7 no.1
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• pp.79-88
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• 1993

This study deals with the case of a fixed floating structure(FFS) at the mouth of a rectangular harbor under the action of waves represented by the linear wave theory. Modified forms of the mild-slope equation is applied to the propagation of regular wave over constant water depth. The model is extended to include bottom friction and boundary absorption. A hybrid element approximation is used for calculation of linear wave oscillation in and near coastal harbor. Modification of the model was necessary for the FFS. For the conditions tested, the results of laboratory experiments by Ippen and Goda(1963), and Lee (1969) are compared with the calculated one from this model. The cases of flat cylinderical structures, both fixed and floating, were taken to be in an intermediate water depth.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2633-2635
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• 2003

In this study, we propose a fuzzy polynomial neural networks (FPNN) and a genetically optimized fuzzy polynomial neural networks(GoFPNN) for identification of non-linear system. GoFPNN architecture is designed by a FPNN based on fuzzy set and its structure and parameters are optimized by genetic algorithms. A fuzzy neural networks(FNN) based on fuzzy set divide into two structures that is simplified inference structure and linear inference structure. The proposed FPNN is resulted from integration and extension of simplified and linear inference structure of FNN. The consequence structure of the FPNN consist of polynomials represented by networks using connection weights for rules. The networks comprehend simplified(Type 0), linear (Type 1), and quadratic(Type 3) inferences. The proposed FPNN can select polynomial type of consequence part for each rule. Therefore, proposed scheme can offer flexible structure design capability for a system characteristics. Moreover, GAs is applied to networks structure and parameters tuning of proposed FPNN, and its efficient application method is discussed, these subjects are result in GoFPNN that is optimal FPNN. To evaluate proposed model performance, a numerical experiment is carried out.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.797-803
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• 2001

This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.