• Journal of Advanced Marine Engineering and Technology
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• v.24 no.5
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• pp.97-102
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and the PI controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system in characterized by a nonlinear equation with coulomb friction. The PI controller regulates the error between the set-point and the system output which may be caused by modeling error, variations of parameters and disturbances. The output which may be caused by modeling error, variations of parameters and disturbances. The parameters of the model and the PI controller are adjusted offlinely by a genetic algorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3435-3441
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• 2014

A dynamic modeling and prediction of polyurethane foam material, which is used as the seat in vehicles is very important for improving the ride quality of vehicle occupants. In this study, parameters to define the nonlinear stiffness and time-variant characteristics of the viscoelasticity of polyurethane foam were obtained using a static compression test. Polynomial functions and convolution integral were used to model the nonlinear and viscoelastic characteristics of polyurethane foam mathematically. The dynamic behaviors excited by the seat floor displacement were analyzed using a numerical integration method for the nonlinear vibration model. As a result, the viscoelastic characteristics of polyurethane foam was found to be an important parameter for improving the ride quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5194-5201
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• 2011

In this paper, an adaptive nonlinear friction compensation scheme for second-order nonlinear mechanical system with a partially known nonlinear dynamic friction is proposed to achieve asymptotic position and velocity tracking in the absence of disturbances and modeling errors. It is also shown that even with disturbances and modeling errors, in contrast to existing other adaptive control schemes, by proper adjustment of design parameters, reduced error bounds on position and velocity tracking can be achieved.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.191-200
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• 1996

This paper presetns a new fuzzy neural network for fuzzy modeling.The fuzzy neural network is composed of 4 layers and then odes of each layer represent the each step of the if-then fuzzy inference. A heuristic based on the back-propagation algorithm is proposed to ajdust the parameters of the fuzzy nerual network. We prove the feasibility of the network using the experiments on modeling a nonlinear mathematical system and the comparison with previous research.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.346-350
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• 1996

This paper presents a Neural Network Identification (NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulation system of Left Ventricular Assist Device(LVD). This system consists of electronic circuits and pneumatic driving circuits. The initation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded. System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, Heart Rate(HR), Systole-Diastole Rate(SDR), which can vary state of system, and preload, afterload, which indicate the systemic dynamic characteristics and output parameters are preload, afterload.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.2-102
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• 2002

In this paper, the improved Multi-FNN (Fuzzy-Neural Networks) model is identified and optimized using HCM (Hard C-Means) clustering method and optimization algorithms. The proposed Multi-FNN is based on FNN and use simplified and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and genetic algorithms (GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parame...

• YAKHAK HOEJI
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• v.43 no.2
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• pp.160-168
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• 1999

The purpose of this study was to determine pharmacokinetic parameters of vancomycin using peak and trough plasma level (PTL) and Bayesian analysis in 20 Korean normal volunteers, 16 gastric cancer and 12 lymphoma patients and also using the compartment model dependent (nonlinear least squares regression: NLSR) and compartment model independent (Lagrange) analysis in 10 ovarian cancer patients. Nonparametric expected maximum (NPEM) algorithm for calculation of the population pharmacokinetic parameters was used, and these parameters were applied for clinical pharmacokinetic parameters by Bayesian analysis. Vancomycin was administered as dose of 1.0 g every 12 hrs for 3 days by IV infusion over 60 minutes in normal volunteers, gastric cancer and lymphoma patients. Population pharmacokinetic parameters, K and Vd in gastric cancer and lymphoma patients using NPEM algorithm were $0.158{\pm}0.014{\;}hr^{-1},{\;}0.630{\pm}0.043{\;}L/kg{\;}and{\;}0.131{\pm}0.0261{\;}hr^{-1},{\;}0.631{\pm}0.089{\;}L/kg$ respectively. The K and Vd in gastric cancer and lymphoma patients using Bayesian analysis were $0.151{\pm}0.027,{\;}0.126{\pm}0.056{\;}hr^{-1}{\;}and{\;}0.62{\pm}0.105,{\;}0.63{\pm}0.095{\;}L/kg$. The K and Vd in ovarian cancer patient using the NLSR and Lagrange analysis were $0.109{\pm}0.008,{\;}0.126{\pm}0.012{\;}hr^{-1}{\;}and{\;} 0.76{\pm}0.08,{\;}0.69{\pm}0.19{\;}L/kg$, respectively. It is necessary for effective dosage regimen of vancomycin in cancer patients to use these population parameters.

• Advances in materials Research
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• v.5 no.4
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• pp.245-261
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• 2016

Thermo-mechanical buckling problem of functionally graded (FG) nanoplates supported by Pasternak elastic foundation subjected to linearly/non-linearly varying loadings is analyzed via the nonlocal elasticity theory. Two opposite edges of the nanoplate are subjected to the linear and nonlinear varying normal stresses. Elastic properties of nanoplate change in spatial coordinate based on a power-law form. Eringen's nonlocal elasticity theory is exploited to describe the size dependency of nanoplate. The equations of motion for an embedded FG nanoplate are derived by using Hamilton principle and Eringen's nonlocal elasticity theory. Navier's method is presented to explore the influences of elastic foundation parameters, various thermal environments, small scale parameter, material composition and the plate geometrical parameters on buckling characteristics of the FG nanoplate. According to the numerical results, it is revealed that the proposed modeling can provide accurate results of the FG nanoplates as compared some cases in the literature. Numerical examples show that the buckling characteristics of the FG nanoplate are related to the material composition, temperature distribution, elastic foundation parameters, nonlocality effects and the different loading conditions.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.6
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• pp.98-106
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• 2011

This paper reviews the state-of-the-art thermoacoustic(TA) modeling techniques and research trend to predict major parameters determining combustion instabilities in lean premixed gas turbine combustors. Linear TA modeling results give us an information on eigenfrequencies and initial growth rate of the instabilities. For the prediction, linear relation equation between acoustic waves and heat release oscillations should be derived in the determined system. Key information for this analysis is to determine the heat release fluctuations in the combustor, which is typically obtained by using n-${\tau}$ function from flame transfer function measurements and/or predictions. Great advancement in the linear TA modeling has been made over a couple of decades, and some successful prediction results have been reported in actual gas turbine combustors. However nonlinear TA model developments which are required to analyze nonlinear system behaviors such as limit cycle saturation and transition phenomena are still limited in a very simple system. In order to fully understand combustion instabilities in a complicated real system, nonlinear flame dynamics and acoustic wave interaction with nonlinear system boundary conditions should be explained from the nonlinear TA model developments.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1293-1302
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• 1999

In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.