• Title/Summary/Keyword: Robust Parameter Estimation

Search Result 185, Processing Time 0.022 seconds

A Study on the Effect of the Motor Drive in Simulation Analysis by Induction Motor Parameters Using a Matlab & PSPICE (Matlab과 PSPICE를 이용한 유도전동기 파라미터에 의해 전동기 운전에 미치는 영향 분석에 관한 연구)

  • Na, Seung-Kwon;Ku, Gi-Jun
    • Journal of Advanced Navigation Technology
    • /
    • v.16 no.6
    • /
    • pp.1005-1013
    • /
    • 2012
  • In this paper, the induction motor in indirect vector control method modeling, from indirect vector control method it undergoes an influence to the $1/{\tau}$. When changing a rotor resistance because being like this quality Matlab/Simulink where it will make what kind of effect in speed presumption it led proposed control system used microprosser TMS320C31 DSP for high speed processing. The effectiveness of the proposed system is verified by simulation and experimental results. This result shows highly characteristic speed estimation and robust character of load regulation. and the flux which it follows in change of parameter and speed presumption it was under simulation and get the good result which it comes to get it analyzed.

Matched-target Model Inversion for the Position Estimation of Moving Targets (정합-표적모델 역산을 이용한 기동 표적의 위치 추정)

  • 장덕홍;박홍배;김성일;류존하;김광태
    • The Journal of the Acoustical Society of Korea
    • /
    • v.22 no.7
    • /
    • pp.562-572
    • /
    • 2003
  • A matched-target model inversion method was developed for a passive sonar to estimate the position of moving targets. Based on the well known matched-field processing in underwater acoustics, the method finds target position by matching the measured target directions and frequencies with the corresponding values of the proposed target model. For the efficient and accurate estimations, the parameter searching was accomplished using a hybrid optimizing method, which first starts with a global optimization such as generic algorithm or simulated annealing then applies a local optimization of a simple down hill algorithm. The suggested method was testified using simulations for three different moving scenarios. The simulation results showed that the method is robust in convergence, even under the situation of over 5 times standard deviation of Gaussian distribution of measured error, and is practical in calculation time as well.

Sensorless Speed Control of IPMSM Using an Extended Kalman Filter and Nonlinear and Adaptive Back-Stepping Control Technique (비선형 적응 백스텝핑 제어 기법과 EKF를 적용한 IPMSM의 센서리스 속도 제어)

  • Jeon, Yong-Ho;Cho, Whang
    • The Journal of the Korea institute of electronic communication sciences
    • /
    • v.7 no.6
    • /
    • pp.1413-1422
    • /
    • 2012
  • Adaptive back stepping control technique may provide robust control characteristics under parameter perturbation caused by changing external condition. In order to synthesize a high-precision velocity controller for IPMSM(Interior Permanent Magnet Synchronous Motor) using this method, the period of control loop should be very small. However, because of the resolution of the encoder for speed measurement, control cycle is limited, which makes it difficult to improve the performance of the controller. This paper proposes a velocity controller design method based on nonlinear adaptive back-stepping method to accomplish fast and accurate performance. Here, an EKF(Extended Kalman Filter) method is incorporated for the estimation of the motor speed into the design of a speed controller using adapted back-stepping control technique. The performance of the proposed controller is demonstrated through simulation using PSIM.

A Bayesian Approach to Gumbel Mixture Distribution for the Estimation of Parameter and its use to the Rainfall Frequency Analysis (Bayesian 기법을 이용한 혼합 Gumbel 분포 매개변수 추정 및 강우빈도해석 기법 개발)

  • Choi, Hong-Geun;Uranchimeg, Sumiya;Kim, Yong-Tak;Kwon, Hyun-Han
    • KSCE Journal of Civil and Environmental Engineering Research
    • /
    • v.38 no.2
    • /
    • pp.249-259
    • /
    • 2018
  • More than half of annual rainfall occurs in summer season in Korea due to its climate condition and geographical location. A frequency analysis is mostly adopted for designing hydraulic structure under the such concentrated rainfall condition. Among the various distributions, univariate Gumbel distribution has been routinely used for rainfall frequency analysis in Korea. However, the distributional changes in extreme rainfall have been globally observed including Korea. More specifically, the univariate Gumbel distribution based rainfall frequency analysis is often fail to describe multimodal behaviors which are mainly influenced by distinct climate conditions during the wet season. In this context, we purposed a Gumbel mixture distribution based rainfall frequency analysis with a Bayesian framework, and further the results were compared to that of the univariate. It was found that the proposed model showed better performance in describing underlying distributions, leading to the lower Bayesian information criterion (BIC) values. The mixed Gumbel distribution was more robust for describing the upper tail of the distribution which playes a crucial role in estimating more reliable estimates of design rainfall uncertainty occurred by peak of upper tail than single Gumbel distribution. Therefore, it can be concluded that the mixed Gumbel distribution is more compatible for extreme frequency analysis rainfall data with two or more peaks on its distribution.

Application of Inference Models for Estimating Parameters of a Catchment Modelling System (추론모델을 통한 강우-유출모형 매개변수의 간접추정법 적용)

  • Choi, Kyung-Sook
    • Journal of Korea Water Resources Association
    • /
    • v.36 no.4
    • /
    • pp.587-596
    • /
    • 2003
  • Application of a catchment modelling system requires recorded information to ascertain the reliability and robustness of the predicted flow conditions. Where this recorded information is not available, the necessary information for reliable and robust predictions must be obtained from other available information sources. The alternative approach presented in this paper used inference models for getting this necessary information that is required to calibrate and validate the catchment modelling system for both an ungauged and a gauged catchments. In this study, inference models were developed for determination of control parameters of the Storm Water Management Model (SWMM), mainly based on landuse component of the catchment, which is a major factor to impact on quantity and quality of catchment runoff. Results from the study show that the new approach for determination of the spatially variable control parameters produced more accurate estimates than a traditional approach. Also, the number of control parameters estimated can be reduced significantly as the proposed method only requires determination of control parameters associated with each land use of the catchment while a traditional approach needs to assign a number of control parameters for a number of subcatchment.