• Journal of Power Electronics
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• v.18 no.2
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• pp.492-501
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• 2018

In the rotor-flux oriented control used in induction motors, the electrical parameters of the motors should be identified. Among these parameters, the mutual inductance and rotor resistance should be accurately tuned for better operations. However, they are more difficult to identify than the stator resistance and stator transient inductance. The rotor resistance and mutual inductance can change in operations due to flux saturation and heat generation. When detuning of these parameters occurs, the performance of the control is degenerated. In this paper, a novel method for the concurrent identification of the two parameters is proposed based on recursive least square estimation and model reference adaptive control.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.2
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• pp.60-66
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• 2003

For the estimation of chatter lobe boundary it is very important to calculate the natural mode of cutting process. There are many time series algorithms for getting the natural mode of structural endmilling dynamics considering the cutting process. In this study, we have compared several time series methods such as AR algorithm, ARX, ARMAX, ARMA, Box Jenkins, Output Error, Recursive ARX, Recursive ARMAX considering the sampling frequency. As a results, the ARX, ARMAX and IV 4 are more desirable algorithms for the calculation of modal parameters such as natural frequency and damping ratio In endmilling operation. Also these algorithms may be adopted for the natural mode estimation of endmilling operation for chatter lobe prediction.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.6
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• pp.575-583
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• 2000

In this paper, a novel estimation alogrithm of mechanical parameters in two-mass system proposed. The inertia of a load and a motor and the stiffness are estimated by using RLS(Recursive Least Square) algorithm and acceleration information of motor. The effectiveness of the proposed scheme is verified with simulation and experiments results.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.277-288
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• 1997

For the analysis of survival data including covariates whose effects vary in time, the multiprocess discount survival model is proposed. The parameter vector modeling the time-varying effects of covariates is to vary between time intervals and its evolution between time intervals depends on the perturbation of the next time interval. The recursive estimation of the parameter vector can be obtained at the end of each time interval. The retrospective estimation of the survival function and the forecasting of the survival function of individuals of the specific covariates also can be obtained based on the information gathered until the end of the time interval.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.14-20
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• 2020

This paper presents a vehicle mass and road grade estimator for developing an automated bus. To consider the dynamic characteristics of a bus varying with the number of passengers, the longitudinal controller needs the estimation of the vehicle's mass and road grade in real-time and utilizes the information to adjust the control gains. Discrete Kalman filter is applied to estimate the time-varying road grade, and the recursive least squares algorithm is adopted to account for the constant mass estimation. After being implemented in MATLAB/Simulink, the estimators are evaluated with the dynamic model and experimental data of the target bus. The proposed estimators will be applied to complement the algorithm of the longitudinal controller and proceed with algorithm verification.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.1
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• pp.49-52
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• 2020

Since power plant is an important system to provide electricity, it is necessary to monitor it in order to operate safely. Much information related with machine diagnosis exists in written form instead of digital data. So, it causes difficulties of analyzing and finding solutions. Rulebased expert system can provide flexible and effective solutions to users. In this paper, Recursive Bayesian Estimation is applied in order to increase accuracy of solutions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.475-481
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• 1999

This paper presents a recursive least square estimation algorithm to estimate parameters of the vector controlled induction machine based on measurements of the stator voltage, curents and slip frequency. Due to its recursive structure, this algorithm has the potential to be used for on-line estimation and adaptive control. The algorithm is designed using regression model derived from the motor electrical equation. This model is valid when there is a tittle-scale separation between vector control system and adaptive system. Vector control performed at fast stage and slow stage is in charge of parameters estimation. The performance of tile algorithm is illustrated by means of simulation results and experiment.

• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• Journal of Drive and Control
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• v.13 no.4
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• pp.59-67
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• 2016

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.375-381
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• 2005

This paper presents linear algebraic equations in the form of recursive formula to compute elastokinematic characteristics of a suspension system. Conventional methods of elastokinematic analysis are based on nonlinear kinematic constrant equations and force equilibrium equations for constrained mechanical systems, which require complicated and time-consuming implicit computing methods to obtain the solution. The proposed linearized elastokinematic equations in the form of recursive formula are derived based on the assumption that the displacements of elastokinematic behavior of a constrained mechanical system under external forces are very small. The equations can be easily computerized in codes, and have the advantage of sharing the input data of existing general multi body dynamic analysis codes. The equations can be applied to any form of suspension once the type of kinematic joints and elastic components are identified. The validity of the method has been proved through the comparison of the results from established elastokinematic analysis software. Error estimation and analysis due to piecewise linear assumption are also discussed.