• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1874-1881
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• 2010

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.452-459
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• 2021

Estimating the mutual time delay between two acoustic sensors is used in various fields such as tracking and estimating the location of a target in room acoustics and sonar. In the time delay estimation methods, there are a non-parametric method, such as Generalized Cross Correlation (GCC), and a parametric method based on system identification. In this paper, we propose a time delay estimation method based on the parametric method. In particular, we propose a method that considers the noise in each receiving acoustic sensor. Simulation confirms that the proposed algorithm is superior to the existing generalized cross-correlation and adaptive eigenvalue analysis methods in white noise and reverberation environments.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.261-264
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• 1993

This paper proposes an adaptive generalized predictive control with feedforward algorithm for steam generator level control in nuclear power plant. The proposed algorithm is shown that the parameters of N-step ahead predictors can be obtained using the parameters of one-step ahead predictor which is derived from plant model with feedforward. Using this property the proposed scheme is an adaptive algorithm which consists of GPC method and the recursive least squares algorithm for identifying the parameters of one-step ahead predictor. Also, computer simulations are performed to evaluate the performance of proposed algorithm using a mathematical model of PWR steam generator Simulation results show good performances for load variation. And the proposed algorithm shows better responses than PI controller does.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.60-63
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• 1990

This note presents a direct adaptive pole assignment control for general discrete, linear, time-invariant, nonmimum phase system.Controller parameters are estimated from the recursive least-squares algorithm, and some additional auxiliary parameters are obtained from aset of recursive equations based on a certain polynomial identity which is derived from the pole assignment equation and the Bezout identity. This scheme increase the numerical stability of the auxiliary parameters, and guarantees local convergence without any extra conditions for the external input. The effectiveness of the proposed scheme is demonstrated by the computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.176-181
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• 1992

In this paper, an adaptive generalized predictive control(GPC) algorithm which minimizes a N-stage cost function is proposed. The resulting controller is based on GPC algorithm and can be used in unknown plant parameters as the parameters of one step ahead predictor are estimated by recursive least squares method. The estimated parameters are extended to G,P, and F amtrix which contain the parameters of N step ahead predictors. And the minimization of cost function assuming no constraints on future controls results in the projected control increment vector. Hence this adaptive GPC algorithm can be used for either unknown system or varing system parameters, and it is also shown through simulations that the algorithm is robust to the variation of system parameters. This adaptive GPC scheme is shown to have the same stability properties as the deterministic GPC, and requires small amount of calculation compared to other adaptive algorithms which minimize N-stage cost function. Especially, in case that the maximum output horizon is 1, the proposed algorithm can be applicable to direct adaptive GPC.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.328-332
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• 1993

This study presents an algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is applied for the weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined form the step input. The performance of the algorithm was tested on a check weigher. Discussions were extended to the development of noise reduction techniques and to the lagged introduction of objects on the moving plate. It turns out that the algorithm shows several desirable features suitable for microcomputer assisted real-time signal processing, which are high precision and stability in noisy environment.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.78-85
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• 1996

The set of admissible time-variations in the input signal can be separated into two categories : slow parameter changes and large parameter changes which occur infrequently. A common approach used in the tracking of slowly time-varying parameters is the exponential recursive least-squares(RLS) algorithm. There have been a variety of research works on the error analysis of the exponential RLS algorithm for the slowly time-varying parameters. In this paper, the focus has been given to the error analysis of exponential RLS algorithms for the input data with abrupt property changes. The voiced speech signal is chosen as the principal application. In order to analyze the error performance of the exponential RLS algorithm, deterministic properties of the exponential RLS algorithms is first analyzed for the case of abrupt parameter changes, the impulsive input(or error variance) synchronous to the abrupt change of parameter vectors actually enhances the convergence of the exponential RLS algorithm. The analysis has also been verified through simulations on the synthetic speech signal.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.240-240
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• 2000

This paper presents two algorithms based on the concept of the frequency domain adaptive filter(FDAF). First the frequency domain recursive least squares(FRLS) algorithm with the overlap-save filtering technique is introduced. This minimizes the sum of exponentially weighted square errors in the frequency domain. To eliminate discrepancies between the linear convolution and the circular convolution, the overlap-save method is utilized. Second, the sliding method of data blocks is studied Co overcome processing delays and complexity roads of the FRLS algorithm. The size of the extended data block is twice as long as the filter tap length. It is possible to slide the data block variously by the adjustable hopping index. By selecting the hopping index appropriately, we can take a trade-off between the convergence rate and the computational complexity. When the input signal is highly correlated and the length of the target FIR filter is huge, the FRLS algorithm based on the block processing technique has good performances in the convergence rate and the computational complexity.