• Journal of Power Electronics
• /
• 제17권2호
• /
• pp.398-410
• /
• 2017

This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate ($LiFePO_4$) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

• 대한수학회보
• /
• 제33권3호
• /
• pp.377-383
• /
• 1996

We consider in this paper the following nonlinear regression model $$ (1.1) y_t = f(x_t, \theta_o) + \in_t, t = 1, \ldots, n, $$ where $y_t$ is the tth response, $x_t$ is m-vector imput variable, $\theta_o$ is a p-vector of unknown parameter belong to a parameter space $\Theta, f:R^m \times \Theta \ to R^1$ is a nonlinear known function, and $\in_t$ are independent unobservable random errors with finite second moment.

• 대한기계학회논문집
• /
• 제16권6호
• /
• pp.1036-1055
• /
• 1992

본 연구에서는 일단앞섬 상태추정기(one-step ahead state estimator)를 사용 하는 방법을 제안하였다. 이 방법은 비선형 시스템을 궤환 선형화하여 등가 선형모 델을 구한 후, 이 등가 선형 모델에 일단앞섬 상태추정기를 구성하여 디지틀 제어를 실현하는 방법으로서, 계측할 수 없는 상태를 추정할 수 있을 뿐 아니라, 선형시스템 에 대한 상태 추정기이므로 다음 단계의 상태를 비교적 정확하게 추정할 수 있어서 궤 환 선형화시 요구되는 연속적인 상태 궤환에 근사시킬 수 있고, 직접 궤환 선형화된 등가 선형 모델의 상태를 추정할 수 있으므로, 변환시의 연산량도 줄일 수 있다. 이 로서 Grizzle이나 Lee의 방법보다 간단하게 궤환 선형화에 의한 디지틀 상태 제어기를 구현할 수 있었다.

• 전기학회논문지
• /
• 제56권5호
• /
• pp.944-957
• /
• 2007

In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it and other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstopping control method.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.275-280
• /
• 2005

The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
• /
• pp.1041-1044
• /
• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

• 대한기계학회논문집
• /
• 제16권9호
• /
• pp.1722-1727
• /
• 1992

본 연구에서는 이러한 불확실한 시스템에 대하여 정규화 형태의 추정기를 적 용한 적응 입출력 선형화 제어기의 설계에 대해 연구하였으며, 신호 성장속도(signal growth rates)의 개념을 도입하여 안정성을 해석하였다. 정규화 형태의 추정기를 적 용함으로써 큰 불확실성에 대해 보다 안정한 수렴 특성을 얻을 수 있음을 시뮬레이션 을 통해 보였다.

• 한국인터넷방송통신학회논문지
• /
• 제12권1호
• /
• pp.151-156
• /
• 2012

근사분산을 최대화하는 least favorable한 ${\epsilon}$-contaminated 정규분포는, 중간 영역에서는 가우시안이나 그 외의 영역에서는 라플라시안 분포를 갖는다는 사실에 근거하여 본 논문에서는 이 확률분포 하에서 비선형 잡음제거 알고리즘을 유도하고 이의 성능을 확인한다. 제안 알고리즘은 위 잡음 환경에서 MLE(maximum likelihood estimator) 이며, efficacy를 최대화한다는 기준에서 최적임을 증명한다. 또한, 유도한 필터를 미리어드 필터와 결합함으로써 임펄스 잡음을 효과적으로 제거하기 위한 비선형 필터를 제안하고 이를 이론적으로 분석한 다음 ${\alpha}$-stable 확률분포를 갖는 잡음에 열화된 이미지를 이용하여 그 성능을 확인한다.

• 대한수학회보
• /
• 제36권3호
• /
• pp.451-457
• /
• 1999

This paper provides sufficient conditions which ensure the strong consistency of regression quantiles estimators of nonlinear regression models. The main result is supported by the application of an asymptotic property of the least absolute deviation estimators as a special case of the proposed estimators. some example is given to illustrate the application of the main result.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.1553-1556
• /
• 1997

In the paper, we porpose a new mehtod of extending PLS(Partial Least Squares) regressiion method to nonlinear framework and apply it to the estimation of product compositions in high-purity distillation column. There have veen similar efforets to overcome drawbacks of PLS by using nonlinear-mapping ability of meural networks, however, they failed to show great improvement over PLS since they focused only in capturing nonlinear functional relationship between input data, not on nonlinear correlation inthe data set. By incorporating the structure of Robust Auto Associative Networks(RAAN) into that of previous nonlinear PLS, we can handle nonlinear correlation as well as nonlinear functional relationship. The application result shows that the proposed method performs better than previous ones even for nonlinearities caused by changing operating conditions, limited observations, and existence of meas-unrement noises.