• Journal of Mechanical Science and Technology
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• 제16권8호
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• pp.1053-1063
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• 2002

This paper presents a robust nonlinear controller for a f degree of freedom (DOF) parallel manipulator in the task space coordinates. The proposed control strategy requires information on orientations and translations in the task space unlike the joint space or link space control scheme. Although a 6 DOF sensor may provide such information in a straightforward manner, its cost calls for a more economical alternative. A novel indirect method based on the readily available length information engages as a potential candidate to replace a 6 DOF sensor. The indirect approach generates the necessary information by solving the forward kinematics and subsequently applying alpha-beta-gamma tracker With the 6 DOF signals available, a robust nonlinear task space control (RNTC) scheme is proposed based on the Lyapunov redesign method, whose stability is rigorously proved. The performance of the proposed RNTC with the new estimation scheme is evaluated via experiments. First, the results of the estimator are compared with the rate-gyro signals, which indicates excellent agreement. Then, the RNTC with on-line estimated 6 DOF data is shown to achieve excellent control performance to sinusoidal inputs, which is superior to those of a commonly used proportional-plus-integral-plus-derivative controller with a feedforward friction compensation under joint space coordinates and the nonlinear controller under task space coordinates.

• 제어로봇시스템학회논문지
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• 제12권2호
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• 대한기계학회논문집A
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• 제35권11호
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• pp.1423-1428
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• 2011

본 논문은 비선형 구동기를 포함한 유연시스템의 잔류변위저감을 위한 학습입력성형제어기를 제시한다. 제시되는 제어기는 비선형 구동기에 대한 입력성형제어기, 반복최소자승법 및 설계변수 updating rule 을 통합하여 개발된다. 비선형 구동기에 대응한 입력성형제어기 설계변수의 updating mechanism 을 개선하기 위한 잔류변위 측정함수가 제시된다. 제시된 제어방법을 pendulum system 에 적용하여 변수추정의 수렴성과 변위저감제어성능의 평가를 통해 수치해석적으로 실용성이 검증된다.

• 대한수학회지
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• 제38권3호
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• pp.523-559
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• 2001

To predict the fate of groundwater contaminants, accurate spatially continuous information is needed. Because most field sampling of groundwater contaminants are not conducted spatially continuous manner, a special estimation technique is required to interpolate/extrapolate concentration distributions at unmeasured locations. A practical three-dimensional estimations method for in situ groundwater contaminant concentrations is introduced. It consistas of two general steps: estimation of macroscopic transport process and kriging. Using field data and nonlinear optimization techniques, the macroscopic behavior of the contaminant plume is estimated. A spatial distribution of residuals is obtained by subtracting the macroscopic transport portion from field data, then kriging is applied to estimate residuals at unsampled locations. To reduce outlier effects on obtaining correlations between residual data which are needed for determining variougram models, the R(sub)p-estimator is introduced. The proposed estimation method is applied to a field data set.

• 한국정밀공학회지
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• 제21권2호
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• pp.35-42
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• 2004

The control systems with friction cause the steady state error and slow response, because friction is a sensitive to the change of system condition and has highly nonlinear characteristics. To overcome these problems and do precise position control for a ball-screw system, we use Coulomb friction estimator and the sliding mode control(SMC) to compensate its negative effect. The applied SMC for tracking position has a characteristics of robust stability and reducing chattering, and is derived from the Lyapunov stability theorem and reaching condition. Compensating the estimated friction torque to the bounded disturbance term of the SMC's equivalent control input, it has a tracking performance better than the PID from the experimental results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1740-1741
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• 2011

This paper proposes a new linear recursive target state estimator for automotive collision warning system. The target motion is modeled in Cartesian coordinate system while the radar measurements such as range, line-of-sight angle and range rate are obtained in polar coordinate system. To solve the problem by nonlinear relation between these two coordinate system, a practical linear filter design scheme employing the predicted line-of-sight Cartesian coordinate system (PLCCS) is proposed. Especially, PLCCS can effectively incorporate range rate measurements into target tracking system. It is known that the utilization of range rate measurements enables the improvement of target tracking performance. Moreover, PLCCS based target tracking system is implemented by linear recursive filter structure and hence is more suitable scheme for the development of reliable collision warning system. The performance of the proposed method is demonstrated by computer simulations.

• International Journal of Precision Engineering and Manufacturing
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• 제3권4호
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• pp.94-100
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• 2002

The conventional sliding mode control (SMC) technique requires a priori knowledge of the upperbounds of disturbances and/or modeling uncertainties to assure robustness. This, however, may not be easy to obtain in practical situation. This paper presents a new methodology, a sliding mode control with disturbance estimator (SMCDE), which offers a robust control performance without a priori knowledge about the disturbance. The proposed technique is featured by an average value of the imposed disturbance over a certain period. A nonlinear spring-mass-damper system and a two-link robot system are adopted as illustrative application examples. Control performances such as estimation error and tracking error are compared between the proposed methodology and conventional scheme.

• 전력전자학회논문지
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• 제17권3호
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• pp.222-229
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• 2012

The SOC estimation method based on Kalman Filter(KF) requires the accurate battery model to express the electrical characteristics of the battery. However, the performance of KF SOC estimator can hardly be improved because of the nonlinear characteristic of the battery. This paper proposes the new KF SOC estimator of Lithium-Polymer Battery(LiPB), which considers the variation of parameters based on the hysteresis effect, the magnitude of SOC, the charging/discharging mode and the on/off load conditions. The proposed SOC estimation method is verified with the PSIM simulation combined the experimental data of the LiPB.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.408-413
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• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).

• 대한기계학회논문집A
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• 제30권8호
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• pp.880-888
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• 2006

This paper presents the sliding mode control with the perturbation estimator for a nonlinear control system in the presence of perturbations including external disturbances, unpredictable parameter variations, ana unstructured dynamics. The proposed perturbation estimator is based on the Recursive Linear Smoothed Newton predictive algorithm so that it is effective to attenuate an undesired noise in high frequency band and to predict the present perturbation signal from the previous ones. Compared to conventional sliding mode control (SMC) and sliding mode control with perturbation estimation (SMCPE) introduced by Elmali and Olgac, the control algorithm proposed in this study can offer better tracking control performances and more feasible estimation characteristics. The effectiveness and superiority of the proposed control strategy are demonstrated by a series of simulations on the position tracking control of a simple two-link robot manipulator subject to velocity feedback signals including white noises.