• 제목/요약/키워드: Modeling parameter

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LPD(Linear Parameter Dependent) System Modeling and Control of Mobile Soccer Robot

  • Kang, Jin-Shik;Rhim, Chul-Woo
    • International Journal of Control, Automation, and Systems
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    • 제1권2호
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    • pp.243-251
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    • 2003
  • In this paper, a new model for mobile soccer robot, a type of linear system, is presented. A controller, consisting of two loops the one of which is the inner state feedback loop designed for stability and plant be well conditioned and the outer loop is a well-known PI controller designed for tracking the reference input, is suggested. Because the plant, the soccer robot, is parameter dependent, it requires the controller to be insensitive to the parameter variation. To achieve this objective, the pole-sensitivity as a pole-variation with respect to the parameter variation is defined and design algorithms for state-feedback controllers are suggested, consisting of two matrices one of which is for general pole-placement and other for parameter insensitive. This paper shows that the PI controller is equivalent to the state feedback and the cost function for reference tracking is equivalent to the LQ cost. By using these properties, we suggest a tuning procedure for the PI controller. We that the control algorithm in this paper, based on the linear system theory, is well work by simulation, and the LPD system modeling and control are more easy treatment for soccer robot.

마이크로 유전알고리즘을 이용한 적운물리과정 모수 최적화에 따른 여름철 강수예측성능 개선 (The Improvement of Summer Season Precipitation Predictability by Optimizing the Parameters in Cumulus Parameterization Using Micro-Genetic Algorithm)

  • 장지연;이용희;최현주
    • 대기
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    • 제30권4호
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    • pp.335-346
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    • 2020
  • Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

DFT 기반의 시스템 모델링을 이용한 DC Motor의 위치제어 (The Position Control of DC Motor using the System Modeling based on the DFT)

  • 안현진;심관식;임영철;남해곤;김광헌;김의선
    • 전기학회논문지
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    • 제61권4호
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    • pp.542-548
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    • 2012
  • This study presents a new method of system modeling by using the Discrete Fourier Transform for the position control system of DC Motor. And the proposed method is similar to the method of System Identification by analysis of correlation of the measured input-output data. The measured output signals are transformed to the frequency domain using DFT. The Fourier Spectrum of the transformed signals is used for knowing to the feature of having an important effect on the system. And transfer function of the second order system is estimated by the dominant parameter which is computed in the magnitude and the phase of Fourier spectrum of the transformed signals. In addition, the output signal includes the unique feature of system. So, although the basic parameter of the system is unknown for us, the proposed method has an advantage to system modeling. And the controller is easily designed by the estimated transfer function. Thus, in this paper, the proposed method is applied to the system modeling for the position control system of DC Motor and the PD-controller is designed by the estimated model. And the efficiency and the reliability of the proposed method are verified by the experimental result.

S-파라미터를 사용한 클락 그리드 네트워크의 분석과 모델링 (Analysis and Modeling of Clock Grid Network Using S-parameter)

  • 김경기
    • 대한전자공학회논문지SD
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    • 제44권12호
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    • pp.37-42
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    • 2007
  • 클락 그리드 네트워크(Clock Grid Network)는 대부분의 고속 마이크로 프로세서에서 클락 스큐를 줄이기 위한 일반적인 방법이다. 본 논문은 클락 그리드의 모델링과 분석을 위해서 S-파라미터(Scattering Parameter)를 사용한 새로운 효과적인 방법을 제안한다. 또한, 그리드 사이즈와 와이어(wire) 폭이 그리드의 클락 스큐에 미치는 영향을 제시한다. 본 논문에서 클락 그리드의 상호 연결은 RC 수동소자에 의해서 모델화 되고, 제안된 방법의 결과는 Hspice의 시뮬레이션 결과와 비교해서 10 % 내의 오차를 보여준다.

전자기 리니어 액츄에이터의 집중매개변수 모델링 및 해석 (Lumped Parameter Modeling and Analysis of Electromagnetic Linear Actuator)

  • 장재환;조성진;김진호
    • 한국기계가공학회지
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    • 제15권5호
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    • pp.18-24
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    • 2016
  • An electromagnetic linear actuator is controlled precisely and securely and is useful in devices that require linear motion. The most commonly used method in the performance verification process for an electromagnetic actuator is finite element analysis that utilizes CAE. However, finite element analysis has the disadvantage that modeling and analysis consume a lot of time. Accordingly, lumped parameter analysis can be an alternative approach to the finite element method because of its computation iteration capability with fair accuracy. In this paper, the lumped parameter model and simulation results are presented. In addition, the results of the lumped parameter analysis are compared with those obtained from finite element analysis for verification.

Common Model EMI Prediction in Motor Drive System for Electric Vehicle Application

  • Yang, Yong-Ming;Peng, He-Meng;Wang, Quan-Di
    • Journal of Electrical Engineering and Technology
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    • 제10권1호
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    • pp.205-215
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    • 2015
  • Common mode (CM) conducted interference are predicted and compared with experiments in a motor drive system of Electric vehicles in this study. The prediction model considers each part as an equivalent circuit model which is represented by lumped parameters and proposes the parameter extraction method. For the modeling of the inverter, a concentrated and equivalent method is used to process synthetically the CM interference source and the stray capacitance. For the parameter extraction in the power line model, a computation method that combines analytical method and finite element method is used. The modeling of the motor is based on measured date of the impedance and vector fitting technique. It is shown that the parasitic currents and interference voltage in the system can be simulated in the different parts of the prediction model in the conducted frequency range (150 kHz-30 MHz). Experiments have successfully confirmed that the approach is effective.

연속계의 이산화를 위한 새로운 모델링 기법 (A new modeling technique for the distributed parameter system - digital modeling approach)

  • 이용관;김인수;홍성욱
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 1995년도 추계학술대회논문집; 한국종합전시장, 24 Nov. 1995
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    • pp.227-232
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    • 1995
  • This paper presents a digital modeling technique for the distributed parameter system. The basic idea of the proposed technique is to discretize a continuous system with respect to the spatial coordinate using the approximate methods such as bilinear method and backward difference method. The response of the discretized system is analyzed by Laplace transform and Z transform. The computational result of the proposed technique in a torsional shaft is compared with the exact solution and the result of the finite element method.

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mGA를 사용한 복잡한 비선형 시스템의 뉴로-퍼지 모델링 (Neuro-Fuzzy Modeling of Complex Nonlinear System Using a mGA)

  • 최종일;이연우;주영훈;박진배
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2000년도 하계학술대회 논문집 D
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    • pp.2305-2307
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    • 2000
  • In this paper we propose a Neuro-Fuzzy modeling method using mGA for complex nonlinear system. mGA has more effective and adaptive structure than sGA with respect to using the changeable-length string. This paper suggest a new coding method for applying the model's input and output data to the number of optimul rules of fuzzy models and the structure and parameter identifications of membership function simultaneously. The proposed method realize optimal fuzzy inference system using the learning ability of Neural network. For fine-tune of the identified parameter by mGA, back-propagation algorithm used for optimulize the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through compare with ANFIS.

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Effectiveness of Sensitivity Analysis for Parameter Selection in CLIMEX Modeling of Metcalfa pruinosa Distribution

  • Byeon, Dae-hyeon;Jung, Sunghoon;Mo, Changyeun;Lee, Wang-Hee
    • Journal of Biosystems Engineering
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    • 제43권4호
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    • pp.410-419
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    • 2018
  • Purpose: CLIMEX, a species distribution modeling tool, includes various types of parameters representing climatic conditions; the estimation of these parameters directly determines the model accuracy. In this study, we investigated the sensitivity of parameters for the climatic suitability calculated by CLIMEX for Metcalfa pruinosa in South Korea. Methods: We first changed 12 parameters and identified the three significant parameters that considerably affected the CLIMEX simulation response. Results: The result indicated that the simulation was highly sensitive to changes in lower optimal temperatures, lower soil moisture thresholds, and cold stress accumulation rate based on the sensitivity index, suggesting that these were the fundamental parameters to be used for fitting the simulation into the actual distribution. Conclusion: Sensitivity analysis is effective for estimating parameter values, and selecting the most important parameters for improving model accuracy.

Generalized Fuzzy Modeling

  • Hwang, Hee-Soo;Joo, Young-Hoon;Woo, Kwang-Bang
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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    • pp.1145-1150
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    • 1993
  • In this paper, two methods of fuzzy modeling are prsented to describe the input-output relationship effectively based on relation characteristics utilizing simplified reasoning and neuro-fuzzy reasoning. The methods of modeling by the simplified reasoning and the neuro-fuzzy reasoning are used when the input-output relation of a system is 'crisp' and 'fuzzy', respectively. The structure and the parameter identification in the modeling method by the simplified reasoning are carried out by means of FCM clustering and the proposed GA hybrid scheme, respectively. The structure and the parameter identification in the modeling method by the neuro-fuzzy reasoning are carried out by means of GA and BP algorithm, respectively. The feasibility of the proposed methods are evaluated through simulation.

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