• 전기학회논문지
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• 제59권9호
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• pp.1600-1604
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• 2010

This paper presents the optimum design of the brushless motor to maximize the output power per weight considering the design parameter tolerance. The optimization is proceeded by commercial software that is adopted the scatter-search algorithm and the characteristic analysis is conducted by FEM. The stochastic optimum design results are compared with those of the deterministic optimization method. We can verify that the results of the stochastic optimization is superior than that of deterministic optimization.

• 대한전기학회논문지
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• 제39권4호
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• pp.380-385
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• 1990

This Paper presents a general algorithm for the parameter estimation of an antoregressive moving average process observed in additive white noise. The algorithm is based on the Gauss-Newton recursive prediction error method. For the parameter estimation, the output measurement is modelled as an innovation process using the spectral factorization, so that noise free RPE ARMA estimation can be used. Using apriori known properties leads to algorithm with smaller computation and better accuracy be the parsimony principle. Computer simulation examples show the effectiveness of the proposed algorithm.

• E2M - 전기 전자와 첨단 소재
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• 제9권8호
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• pp.799-805
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• 1996

In short channel MOSFET, it is very important to establish optimal process conditions because of variation of device characteristics due to the process parameters. In this paper, we used process simulator and device characteristics caused by process parameter variation. From this simulation, it has been ' derived to the dependence relations between process parameters and device characteristics. The experimental result of fabricated short channel device according to the optimal process parameters demonstrate good device characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.706-709
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• 2005

We study the adaptive stabilization of the Van der Pol equation. A parameter update law is designed by the immersion and invariance method, and is used in conjunction with both the feedback linearization and backstepping control laws. Simulation results show that the responses obtained in the adaptive case are very similar to the known parameter case, and the parameter estimator converges to the true value.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.231-235
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• 1995

In this paper, the frozen time approach is used to analyze the nonlinear system with time varying parameter. Using the extended linearization, we propose two analytical methods that compute an upper bound of the Euclidean norm of the difference between state variable and equilibrium point of the given system. The propertise of the two methods are discussed with simple examples.

• 대한전기학회논문지
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• 제37권3호
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• pp.180-185
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• 1988

This paper considers the problem of the optimal input design for parameter estimtion in the ARMAX model in which the system and noise transfer function have the common denominator polynomial. Deriving the information matrix, in detail, for the assumed model structure and using the autocorrelation functin of the filtered input as design variables, it is shown that D-optimal input signal can be realized as an autoregressive moving average process. Computer simulations are carried out to show the standard-deviation reduction in the parameter estimtes using the optimal input signal.

• 대한전기학회논문지
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• 제38권4호
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• pp.316-325
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• 1989

This paper is concerned with robust stabilization of single input or single output systems. Based on the region of non-destabilizing perturbations some approaches to design which allow the given of structured perturbation of plant parameters and their gradient optimization are given. These algorithms iteratively enlarge the stability hypershere in plant parameter space and can be used to design a controller to stabilize a plant subject to given ranges of parameter excursions.

• 대한전기학회논문지:전력기술부문A
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• 제48권7호
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• pp.898-902
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• 1999

In this paper, we proposed an algorithm for estimating parameters of nonlinear continuous-discrete state-space system. This algorithm uses the conventional extended Kalman filter(EKF) for estimating state variables, and modifies the recursive prediction error method for parameter estimation of the nonlinear system. Simulation results for both linear and nonlinear measurements under the environment of process and measurement noises show a convincing performance of the proposed algorithm.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제4B권4호
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• pp.206-210
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• 2004

This paper deals with a parameter estimation of the DC-DC converter system for its diagnosis. Especially, we present the results of parameter estimation for the DC-DC converter model by the system identification method. The parameter estimation for the DC-DC converter system aims at the diagnosis of its operating status. For the operating status diagnosis of the DC-DC converter system, we assume that the DC-DC converter system is an equivalent model of the Buck converter and estimate the main parameter for on-line diagnosis. In addition, for verification of an estimated parameter, we compare a bode plot of the estimated system transfer function and measurement results of the HP4194 instrument. It is a control system analyzer for system transfer function measurement. Our results confirm that the main parameter for diagnosis of the DC-DC converter system can be estimated by the system identification method and that the aging status of the system can be predicted by these results on operating status.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.761-762
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.