• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.33-39
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• 2019

We propose a mechanical parameter estimation algorithm for surface-mounted permanent magnet synchronous motors (SPMSMs) using a sliding-mode observer (SMO) and an adaptive filter. The SMO estimates system disturbances in real time, which contain the information on mechanical parameters. A desirable feature that distinguishes the proposed estimation algorithm from other existing mechanical parameter estimators is that the adaptive filter estimates electromagnetic torque to improve the estimation performance. Moreover, the SMO acts as a low-pass filter to suppress the chattering effect, which enables the smooth output signals of the SMO. We verify the mechanical parameter estimation performance for SPMSM by conducting extensive experiments for the proposed algorithm.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.255-266
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• 2007

A Bayesian estimation of the four-parameter gamma distribution is considered under the noninformative prior. The Bayesian estimators are obtained by the Gibbs sampling. The generation of the shape/power parameter and the power parameter in the Gibbs sampler is implemented using the adaptive rejection sampling algorithm of Gilks and Wild (1992). Also, the location parameter is generated using the adaptive rejection Metropolis sampling algorithm of Gilks, Best and Tan (1995). Finally, the simulation result is presented.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.5
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• pp.12-19
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• 1982

In general the design methods of adaptive filter are divided into two categories, one is based upon the local parameter optimization theory and the other is based upon stability theory. Among the various design techniques, the LMS algorithm by steepest-descent method which is based upon local parameter optimization theory is used widely. In designing the adaptive filter, the most important factor is the convergence rate of the algorithm. In this paper a new algorithm is proposed to improve the convergence rate of adaptive firter compared with the commonly used LMS algorithm. The faster convergence rate is obtained by adjusting the adaptation gain of LMS algorithm. And various aspects of improvement of the adaptive filter characteristics are discussed in detail.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.435-439
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• 2007

This paper deals with RLS algorithm using Newton-Raphson method based adaptive forgetting factor for a passive telemetry RF sensor system in order to estimate the time variant parameter to be included in RF sensor model. For this estimation with RLS algorithm, phasor typed RF sensor system modelled with inductive coupling principle is used. Instead of applying constant forgetting factor to estimate time variant parameter, the adaptive forgetting factor based on Newton-Raphson method is applied to RLS algorithm without constant forgetting factor to be determined intuitively. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.494-498
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• 1989

This paper describes an efficient time-varying parameter estimation algorithm by resetting the parameter and P matrix of the RLS algorithm. The described algorithm is useful for estimating both jump parameter and drifting parameter which vary quite rapidly.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.39-46
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• 1994

In this paper, three types of parameter update law which can be used in model reference adaptive control are suggested based on speed-gradient algorithm which was introduced by Fradkov. It is shown that the parameter update law which was proposed by Narendra is a special from of these laws and that proposed parameter update laws can insure the global stability under some conditions such as attainability and convexity. We also comment that the transfer function of reference model shoud be positive real for the realization of parameter update law.

• Journal of information and communication convergence engineering
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• v.8 no.3
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• pp.307-311
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• 2010

EPCglobal Class-1 Gen-2 protocol has been proposed for UHF-band RFID systems. In Gen-2 standard, Q-algorithm was proposed to select a frame size for the next query round without estimating the number of tags. Therefore, the Q-algorithm has advantage that the reader's algorithm is simpler than other algorithms. However, it is impossible to allocate the optimized frame size. Also, the original Q-algorithm did not define an optimized parameter C for adjusting the frame size. In this paper, we propose an adaptive Q-algorithm with the different parameter $C_c$ and $C_i$ in accordance with the status of reply slot. Simulation results show that the proposed adaptive Q-algorithm outperforms the original Gen-2 Q-algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.39.2-39
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• 2002

This paper presents adaptive add-on control algorithms for theconventional mode of operation of MEMS z-axis gyroscopes. This scheme is realized by adding an outer loop to a conventional force-balancing scheme that includes a parameter estimation algorithm. The parameter adaptation algorithm estimates the angular rate, identifies and compensates the quadrature error, and may permit on-line automatic mode tuning. The convergence and resolution analysis show that the proposed adaptive add-on control scheme prevents the angular rate estimate from being contaminated by the quadrature error, while keeping ideal resolution performance of a conventional force-balancing scheme.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.875-878
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• 1998

In this paper, an adaptive event detection algorithm is proposed, for which we use the statistics of subblock image and adaptive threshold levels. The adaptive threshold level for a parameter binarization is taken by averaging the corresponding paramerter obtained from several input images. As simulation results, it is shown that the proposed algorithm is much more adaptive to the input images and effective in event detection rate than the conventional difference based algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.83-85
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• 1986

The error differential equations which are derived by using the first error model are uniformly asymptotial stable if the input is bounded and sufficiently rich. In the adaptive control, the speed of convergence of system output or parameter error in such cases is of both practical and theoretical interest. In this paper, the adaptive algorithms(Gradient algorithm, Intergral algorithm) are discussed from the point of view of speed convergence and the modification of adaptive law for prohibition of overadaptation is discussed. The result is compared among this algorithms and the adaptive gain is choosed by this result(the speed of convergence).