• 대한기계학회논문집
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• 제14권2호
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• pp.331-338
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• 1990

In adaptive control, the lack of persistent and rich excitation causes the estimated parameters to drift, which degrade the performance of the system and may introduces instability to the system in a stochastic environment. To solve the problem of the parameter drift, the concept of single parameter adaptation is presented. For the parameter identification, a priori error is directly used for adaptation error. The structure of the controller is based upon the minimum variance control technique. The stability and robustness analysis is carried out by the sector stability theorem for the second order system. The computer simulation is performed to justify the theoretical analysis for the various cases.

• 대한전기학회논문지:전력기술부문A
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• 제51권3호
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• pp.118-126
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• 2002

AVR parameter tuning for voltage control of generators has generally been done with the off-line open-circuit model of the synchronous generator. When the generator is connected on-line and operating with load the AVR operates in an entirely different environment from the open-circuit conditions. This paper describes a new method for AVR parameter tuning for on line conditions using SQP(Sequential Quadratic Programming) meshed with frequency response characteristics of linearized on-line system model. As the proposed method uses the un - line system model the tuned parameter sets show more optimal behavior in the on-line operating conditions. furthermore, as this method considers the performance indices that are needed for stable operation as constraints, AVR parameter sets that are tuned by this method could guarantee the stable performance, too.

• 제어로봇시스템학회논문지
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• 제12권6호
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• pp.517-523
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• 2006

This paper is focused on a robust saturation controller for the stable linear time-invariant (LTI) system involving both actuator's saturation and structured real parameter uncertainties. Based on affine quadratic stability and multi-convexity concept, a robust saturation controller is newly proposed and the linear matrix inequality (LMI)-based sufficient existence conditions for this controller are presented. The controller suggested in this paper can analytically prescribe the lower and upper bounds of parameter uncertainties, and guarantee the closed-loop robust stability of the system in the presence of actuator's saturation. Through numerical simulations, it is confirmed that the proposed robust saturation controller is robustly stable with respect to parameter uncertainties over the prescribed range defined by the lower and upper bounds.

• 대한전기학회논문지
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• 제39권7호
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• pp.740-748
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• 1990

In this paper, we consider the problem of parameter estimation, i.e., definding the internal structure of a linear distribution parameter system from its input/output data. First, a linear partial differential equation describing the system is double-integrated with respect to two variables and then transformed into an integral equation. Next the Walsh Operation Matrix for Walsh function and their integration are introduced to transform the integral equation into algebraic simultaneous equations. Finally, we develop an algorithm to estimate the parameters of the linear distributed parameter system from the simple linear algebraic simultaneous equations. It is also shown that our algorithm could be effective in real time data processing since it uses the Fast Walsh Transform.

• 대한전기학회논문지
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• 제40권4호
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• pp.427-438
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• 1991

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic system is preented. In the learning control structure, the control input converges globally and asymtotically to the desired input as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of the time-duration of trajectories, it may be achieved with system trajectories with small duration. In addition, the proposd learning control schemes are applicable to time-varying parametric systems as well as time-invariant systems, because the parameter estimation is performed at each fixed time along the iteration. In the parameter estimator, the acceleration information as well as the inversion of estimated inertia matrix are not used at all, which makes the proposed learning control schemes more feasible.

• 전기학회논문지
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• 제57권10호
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• pp.1861-1868
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• 2008

In this paper, Passive Telemerty RF Sensor System using Unscented Kalman Filter algorithm(UKF) is proposed. General Passive Telemerty RF Sensor System means that it should be "wireless", "implantable" and "batterless". Conventional Passive Telemerty RF Sensor System adopts Integrated Circuit type, but there are defects like complexity of structure and limit of large power consumption in some cases. In order to overcome these kinds of faults, Passive Telemetry RF Sensor System based on inductive coupling principle is proposed in this paper. Because passive components R, L, C have stray parameters in the range of high frequency such as about 200[KHz] used in this paper, Passive Telemetry RF Sensor System considering stray parameters has to be derived for accurate model identification. Proposed Passive Telemetry RF Sensor System is simple because it consists of R, L and C and measures the change of environment like pressure and humidity in the type of capacitive value. This system adopted UKF algorithm for estimation of this capacitive parameter included in nonlinear system like Passive Telemetry RF Sensor System. For the purpose of obtaining learning data pairs for UKF Algorithm, Phase Difference Detector and Amplitude Detector are proposed respectively which make it possible to get amplitude and phase between input and output voltage. Finally, it is verified that capacitive parameter of proposed Passive Telemetry RF Sensor System using UKF algorithm can be estimated in noisy environment efficiently.

• 한국산학기술학회논문지
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• 제13권2호
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• pp.772-778
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• 2012

본 논문은 비선형 시스템의 상태 추정에 널리 사용 되는 Unscented Kalman Filter(UKF)를 활용하여 동역학 시스템의 상태를 추정함과 동시에 파라미터를 추정하였다. 파라미터의 추정은 시스템 제어, 모델링, 성능분석 및 예측 등 다양한 분야에서 매우 중요하다. 공학에서 다루는 대부분의 시스템은 비선형성과 잡음이 존재하므로 파라미터 추정이 매우 어렵다. 이러한 경우에 대하여 본 논문에서는 비선형 필터로서 잡음에 강한 UKF를 이용하여 상태와 파라미터를 추정하였다. 본 논문에서 제안한 파라미터 추정은 기존의 상태방정식에 파라미터 항을 추가하여 확장된 비선형 방정식을 사용하였으며, 진자와 슬라이드로 구성된 2-자유도 동역학 시스템에 적용하였으며, 시스템 운동방정식의 측정 잡음으로 가우시안 잡음을 추가하여 컴퓨터 시뮬레이션을 실시하였다. 시뮬레이션 결과 제안한 방법이 LSM보다 좋은 성능을 보였다. 추정 오차는 3%이내이며, 0.1sec 이내의 수렴하는 것을 확인하였다. 결과적으로 UKF는 상태나 측정 데이터에 잡음이 존재하더라도 시스템의 상태 및 파라미터 추정이 가능하다.

• 한국항만학회지
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• 제14권2호
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• pp.155-163
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• 2000

A discrete system has interpreted by using the network model, and PERT network is one of these methods. For the purpose of analysing the real system, it is necessary to measure the parameter of the real system. And system identification problem is to assume the parameter of a real system when we get to know the system model, the input data and output data. System identification method has been only developed to a system of which a structure has expressed a differential equation or a polynomial expression. But it has been scarcely developed yet in that case of network model. The aim of this paper is to examine a changes when new system is introduced to the present system. The changes are as follows : how the present system will be changed, when the changes will be happened. In this paper, genetic algorithm is used to assume the parameter.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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• pp.101-108
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• 1999

A discrete system has interpreted by using the network model, and PERT network is one of these methods. For the purpose of analysing the real system. it is necessary to measure the parameter of the real system. And system identification problem is to assume the parameter of a real system when we get to know the system model, the input data and output data. System identification method has been only developed to a system of which a structure has expressed a differential equation or a polynomial expression. But it has been scarcely developed yet in that case of network model. The aim of this paper is to examine a changes when new system isn introduced to the present system, The changes are as follows: how the present system will be changed, when the changes will be happened. In this paper, genetic algorithm is used to assume the parameter.

• 대한기계학회논문집A
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• 제21권9호
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• pp.1480-1490
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• 1997

A method by which the system parameter matrices can be estimated from measured time data of excitation force and acceleration has been studied. The acceleration data are integrated numerically to obtain the velocities and displacements, and the systm parameters are estimated from these data by solving equations of motion. The characteristics of the method have been investigated through its application to simulated data of 1 DOF and 2 DOF systems and experimental data measured from a simple structure. It was found that the method is very sensitive to measurement noise and the accuracy of the estimated parameters can be improved by averaging the repeatedly measured data and removing the noise. One of the main advantages of the parameter estimation method is that no a priori information about the system under test is required. The method can be easily extended to non-linear parameter estimation.