• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.215-218
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• 2000

In this paper, a new algorithm for adaptive IIR filters based on equation error methods using the RLS algorithm is proposed. In the proposed algorithm, the concept of feedback of the scaled output error proposed by tin and Unbehauen is employed and the forgetting factor is varied in adaptation process for avoiding the accumulation of the estimation error for additive noise . The proposed algorithm has the good convergence property without the parameter estimation error under the existence of mea-surement noise.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.542-543
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• 2014

In this paper, an estimation scheme based on an augmented sigma-point Kalman filter to estimate the state of charge (SOC) of the battery is presented, where the battery parameters of the series resistance ($R_o$), diffusion capacitance ($C_1$) and resistance ($R_1$) are also estimated through the recursive least squares (RLS) for better accuracy of the SOC. The effectiveness of the proposed method is verified by simulation results.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.101-106
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• 1997

In this paper, sample selective RLS(SSRLS) method is proposed, which aims to eliminate the influence of pitch bias. Its basic concepts are as follows. First it extracts the open glottis interval by using the residual signals, then estimates the formant values from the selected speech samples excluding above open glottis interval. This method has some analogy with the SSLPS, the simulation is conducted upon the synthetic and real speech. From these results, we find more usefulness of the proposed method than the conventional ones.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.383-388
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• 2016

We develop two methods to select a constant in the RLS (Recursive Least Squares) with the convex regularization. The RLS with the convex regularization was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm uses the regularization constant which needs the information about the true channel response for the best performance. In this paper, we propose two methods to select the regularization constant which don't need the information about the true channel response. We show that the estimation performance using the proposed methods is comparable with the Eksioglu and Tanc's algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.517-522
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• 2019

This paper proposes a new motor parameter estimation method. Because the proposed method is based on difference equations, it does not affect the error in the voltage magnitude so called dead-time effect. Information on the motor constant may be needed to improve the motor control performance. For example, a control technique called DTC (Direct Torque Control) requires a motor constant when calculating the torque and flux magnitude. As another example, in the case of predictive control, information on the motor parameters is required to generate voltage references. Because the constant of the motor fluctuates according to the driving environment, it is essential to estimate the correct motor constant because the control performance is degraded when incorrect motor information is used. In the proposed scheme, the motor constant estimated based on the voltage difference equation is obtained using the RLS (Recursive Least Square) technique. The RLS algorithm is applied to obtain the value through an iterative calculation so that the estimation performance is robust to noise. The simulation results carried out with surface mounted permanent magnet motors confirmed the validity of the proposed method.

• Journal of Auto-vehicle Safety Association
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• v.16 no.2
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• pp.51-59
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• 2024

This study presents a method for health monitoring of rotating objects for mobility based on multiple recursive least squares(RLS) algorithms. The performance degradation of the rotating objects causes low handing / low driving performances and even fatal accidents. Therefore, health monitoring algorithm of rotating objects is one of the important technologies for mobility fail-safe and maintenance areas. In order for health monitoring of rotating objects, four recursive least squares algorithms with forgetting factor were designed in this study. The health monitoring algorithm proposed in this study consists of two steps such as uncertainty estimation and parameter changes estimation. In order to improve estimation accuracy, time delay function was applied to the estimated signals based on the first order differential equation and forgetting factors used for the RLS were reasonably tuned. The health monitoring algorithm was constructed in Matlab/Simulink environment and simulation-based performance evaluation was conducted using DC motor model. The evaluation results showed that the proposed algorithm estimates the actual parameter differences reasonably using velocity and current information.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1694-1700
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• 2004

In this paper, we propose the AWLS/MFT (Adaptive Weighed Least Squares/ Modulation Function Technique) devised by A. E. Pearson et al. for the transfer function estimation of a modal system and investigate the performance of several algorithms, the Gram matrix method, a Luenberger Observer (LO), Least Squares (LS), and Recursive Least Squares (RLS), for the estimation of initial conditions. With the benefit of the Modulation Function Technique (MFT), we can separate the estimation problem into two phases: the transfer function parameters are estimated in the first phase, and the initial conditions are estimated in the second phase. The LO method produces excellent IC estimates in the noise free case, but the other three methods show better performance in the noisy case. Finally, we compared our result with the Prony based method. In the noisy case, the AWLS and one of the three methods - Gram matrix, LS, and RLS- show better performance in the output Signal to Error Ratio (SER) aspect than the Prony based method under the same simulation conditions.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.173-176
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• 2000

시변 신호 추적 특성을 향상시키기 위하여, Gauss-Newton Variable Forgetting Factor RLS (GN-VFF-RLS) Algorithm을 제안한다. 최적화된 망각인자를 가정한 기존의 RLS 알고리듬과 비교하여, 제안된 방법은 특히 신호의 변화가 급격히 일어날 경우 주목할만한 추정 성능의 향상을 보여준다. 제안된 알고리듬의 시변 추정 특성을 신호 대 잡음비와 시변 정도에 대하여 모의 실험하고 기존의 추정 알고리듬들과 비교한다.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.488-490
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• 1999

This paper describes the design of self-tuning speed controller for DC motor drive system using RLS(Recursive Least Squares) algorithm and Pole-Placement method. The model parameters, related to inertia and damping coefficient of motor, are estimated on-line by using RLS estimation algorithm. And a control signal is calculated by using pole placement method. Simulation and experimental results show that the proposed controller possesses excellent adaptation capability than a conventional PI/IP controller under parameter change.