• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.399-401
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• 1989

Utilizing an interconnected set of neuron-like elements, the present study is to provide a method of parameter estimation for a second order linear time invariant system of self-tuning controller. The result from the proposed method is evaluated by comparing with those obtained by the recursive least square (RLS) identification algorithm and extended recursive least square (ERLS) algorithm, and it shows that, although the smoothness of system performance is still to be improved, the effectiveness of shorter computing time is demonstrated which may be of considerable value to real time computing.

• 한국통신학회논문지
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• 제38C권3호
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• pp.288-295
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• 2013

본 논문에서는 4S (Ship to Ship, Ship to Shore) 해상통신을 위해 다른 채널 조건 하에서 기존의 채널 추정 기법을 비교하였다. 일반적으로 수신 신호는 다중경로나 부호 간 간섭에 의해 손상을 받게 된다. 시간 변화 다중 페이딩 채널의 추정은 수신기에서 어려운 작업이며, 적절한 채널 추정 필터를 사용함으로써 수신기의 성능을 향상시킬 수 있다. 모의실험은 MATLAB을 사용하여 AWGN (Additive White Gaussian Noise), Rician, Rayleigh 채널에서 채널 추정 알고리즘으로 주로 사용되어지는 LMS (Least Mean Square)와 RLS (Recursive Least-Squares) 알고리즘을 비교 하였다.

• 한국항공운항학회지
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• 제20권4호
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• pp.1-6
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• 2012

The accurate information of mass properties is required for the precise control of the spacecraft. The mass properties, mass and inertia, are changeable by some reasons such as consumption of propellant, deployment of solar panel, sloshing, environmental effect, etc. The gyro-based attitude data including noise and bias reduces the control accuracy so it needs to be compensated for improvement. This paper introduces a real-time inertia estimation method for the attitude determination of STSAT-3, Korea Science Technology Satellite. In this method we first filter the gyro noise with the Extended Kalman Filter(EKF), and then estimate the moment of inertia by using the filtered data from the EKF based on the Recursive Least Square(RLS).

• Journal of Power Electronics
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• 제18권2호
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• pp.492-501
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• 2018

In the rotor-flux oriented control used in induction motors, the electrical parameters of the motors should be identified. Among these parameters, the mutual inductance and rotor resistance should be accurately tuned for better operations. However, they are more difficult to identify than the stator resistance and stator transient inductance. The rotor resistance and mutual inductance can change in operations due to flux saturation and heat generation. When detuning of these parameters occurs, the performance of the control is degenerated. In this paper, a novel method for the concurrent identification of the two parameters is proposed based on recursive least square estimation and model reference adaptive control.

• 전력전자학회논문지
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• 제5권6호
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• pp.575-583
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• 2000

본 논문에서는 이관성 시스템의 기계계 파라미터의 새로운 추정 알고리즘을 제안한다.RLS(Recursive Least Square) 알고리즘과 가속도정보를 이용하여 이관성 시스템의 부하의 관성, 전동기 관성 그리고 축강성을 추정하고 시뮬레이션과 실힘을 통해 제안된 기법의 유효성을 검증한다.

• Structural Engineering and Mechanics
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• 제34권1호
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• 전기학회논문지
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• 제65권6호
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• pp.1070-1079
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• 2016

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

• 한국산학기술학회논문지
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• 제20권4호
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• pp.517-522
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• 2019

본 논문은 전동기 상수 추정 방법을 제안한다. 제안하는 기법은 전동기 전압방정식을 차동식 형태로 전개한 수식에 기반한다. 차동식 형태를 이용함으로써 전동기 상수 추정시 사용되는 전압 정보에 존재하는 크기 오차인 데드타임의 영향을 배제한다. 전동기의 상수값은 전동기 제어 성능 향상을 위해서 필요할 때가 있다. 예를 들면 DTC(Direct Torque Control) 제어 기법에는 토오크 및 자속 크기의 연산시 전동기의 상수를 알아야한다. 다른 예로서 예측제어의 경우 지령치 전압 생성을 위해서는 정확한 전동기의 상수 값의 정보가 필요하다. 전동기의 상수는 구동 환경에 따라 변동하는 값이므로 부정확한 전동기 상수 사용시 제어 성능의 저하를 가져온다. 따라서, 정확한 전동기 상수의 추정이 필요하다. 제안하는 기법에서 전압차동식에 기초하여 추정되는 전동기 상수는 RLS(Recursive Least Square) 기법에 의해 구해진다. 본 연구에서는 단순 수식에 의한 형태로 전동기의 상수를 추정하지 않고, RLS 알고리즘을 적용하여 노이즈에 강인하게 전동기 상수를 추정한다. 표면 부착형 영구자석 전동기의 제어시스템에 적용하여 제안하는 기법의 타당성을 보인다.

• 한국자동차공학회논문집
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• 제19권1호
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• pp.51-57
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• 2011

Information of the lateral velocity and the sideslip angle in a vehicle is very useful in many active vehicle safety applications such as yaw stability control and rollover prevention. Because cost-effective sensors to measure the lateral velocity and the sideslip angle are not available, reliable algorithms to estimation them are necessary. In this paper, a sliding mode observer is designed to estimate the lateral velocity. The side slip angle is estimated using the recursive least square with the disturbance observer and the pseudo integral. The estimated parameters from the combined estimation method are updated recursively to minimize the discrepancy between the model and the physical plant, and any possible effects caused by disturbances. The performance of the proposed monitoring system is evaluated through simulations and experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.203-205
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• 2002

This paper presents the fault location estimation algorithm in transmission line using a recursive least square error method (RLSE). To minimize the computational burden of the digital relay a RLSE approach is used. Computer simulation results of the RLSE algorithm seem promising, indicating that it should be considered for further testing and evaluation.