• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.875-881
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• 2011

This paper describes a new method of estimating the gradient of a function with perturbation and correlation. We impose a known periodic perturbation to the input variable and observe the output of the function in order to obtain much richer and more reliable information. By taking the correlation between the input perturbation and the resultant function outputs, we can determine the gradient of the function. The computation of the correlation does not require derivatives; therefore the gradient can be estimated reliably. Robust estimation of the gradient using perturbation/correlation, which is very effective when an analytical solution is not available, is described. To verify the effectiveness of perturbation/correlation based estimation, the results of gradient estimation are compared with the analytical solutions of an example function. The effects of amplitude of the perturbation and number of samplings in a period are investigated. A minimization of a function with the gradient estimation method is performed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.1009-1019
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• 2006

In this paper, we consider the simultaneous estimation problem for the normal means. We set up the model structure using the several different distributions of the errors for observing their effects of model perturbation for the error terms in obtaining the empirical Bayes and hierarchical Bayes estimators. We compare the performance of those estimators under model perturbation based on a simulation study.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.121-128
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• 2006

This paper deals with the application of enhanced perturbation estimation (SMCEPE) to sliding mode control of a dynamic system in the presence of perturbations including external disturbances, unpredictable parameter variations, and unstructured dynamics. Compared to conventional sliding mode control (SMC) and sliding mode control with perturbation estimation (SMCPE), the proposed one can offer robust control performances under serious control conditions, such as fast dynamic perturbations and slow loop-closure speeds, without a priori knowledge on upper bounds of perturbations. The perturbation estimator in SHCEPE also has more adaptability owing to the fractional-order hold technique. The effectiveness and superiority of the proposed control strategy are demonstrated by a series of simulations on the position tracking control of a two-link robot manipulator.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.477-483
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• 2005

This article describes a robust state estimation method which enables to produce reliable estimates in spite of heavy perturbation including plant uncertainty and external disturbances. The main idea is to combine the standard state estimator with the perturbation observer in the estimator frame. The perturbation observer reflects equivalent quantity of plant uncertainty and external disturbances during the estimation process so that the state estimator dynamics gets as close as possible to the real plant dynamics. The robust state estimator proposed in this paper is given in a recursive discrete-time form which is very useful fur implementation purpose. In terms of the error dynamics derived for the robust state estimator, we discuss the stability issue and noise sensitivity. The effectiveness and practicality of the robust state estimator are verified through numerical examples and experimental results.

• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.535-549
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• 2004

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator(FAPC) to get a good control performance and reduce the chatter, The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network(FAN) The weighting parameters of the compensate. are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.

• Communications of the Korean Mathematical Society
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• v.11 no.3
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• pp.841-853
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• 1996

This article finds smoothed perturbation analysis estimates for the derivatives of mean performance measures in a Markovian queue with batch arrivals. We show that those estimates can be observed from a single sample path.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.866-873
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• 2000

Sliding mode control(SMC) incorporated with perturbation compensation is developed here to reduce the low-frequency tracking error in the presence of wide-band frequency perturbations for a nonlinear dynamic system. The control scheme is designed for estimation of low frequency perturbations with employment of the Time Delay Control and low-pass filter. It is shown that the SMC with perturbation compensation is far superior to the conventional SMC in tracking control of the dynamic systems under model uncertainties and external disturbance conditions.

• Journal of the Korean Statistical Society
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• v.22 no.1
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• pp.27-37
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• 1993

Since the analytic expression for a diagnostic in the logistic regression model is not available, one-step estimation is often used by a case-deletion point of view. In this paper, infinitesimal perturbation approach is used, and it is shown that the scale transformation of infinitesimal perturbation approach is eventually equal to the weighted perturbation of local influence approach and the replacement measure. Also, multiple cases deletion for the masking effect is considered.