• Journal of Navigation and Port Research
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• v.33 no.5
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• pp.323-330
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• 2009

We have been developing an underwater robot for harbour construction using a parallel mechanism The robot is attached to the rope of a crane, which curries a large stone into the undersea The robot's yaw and pitch are controlled by hydraulic cylinders but its roll is uncontrollable. We mount propellers in both side of the robot to generate the roll motion This paper studies on the control for the roll motion of a underwater robot. A gyro-sensor is used to measure the angle in a roll motion We develop the dynamic model to describe the robot's roll motion by a second order non-linear system and identify the model parameters by recursive least square and adaptive identifier. PD control, recursive model based control and adaptive model based control are applied with the dynamic model which computes the control input to compensate disturbances. This paper introduces the underwater robot system and presents the simulated and experimental results of the proposed controller.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.66-72
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• 2003

In the present paper some fundamentals about exceeding the guard interval of an OFDM system is going to discuss and will introduce method to cancel a ISI and ICI effects. In this paper, the performance characteristics of robust OFDM receiving system algorithm that is composed of a channel estimator using recursive least square(RLS) algorithm in time domain and of a ISI and ICI replica and subtracter using the channel coefficients are presented. By computer simulation method, the effect of suppression of ISI and ICI is demonstrated and compared the BER performance with a conventional 1-tap equalized system. Especially the presented scheme improves the BER performance at low SNRs more or so compared with the conventional one-tap equalized receiving configuration.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1146-1151
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• 2003

In this paper, results of an analysis of driving behavior characteristics and a driver-adaptive control algorithm for adaptive cruise control systems have been described. The analysis has been performed based on real-world driving data. The vehicle longitudinal control algorithm developed in our previous research has been extended based on the analysis to incorporate the driving characteristics of the human drivers into the control algorithm and to achieve natural vehicle behavior of the adaptive cruise controlled vehicle that would feel comfortable to the human driver. A driving characteristic parameters estimation algorithm has been developed. The driving characteristics parameters of a human driver have been estimated during manual driving using the recursive least-square algorithm and then the estimated ones have been used in the controller adaptation. The vehicle following characteristics of the adaptive cruise control vehicles with and without the driving behavior parameter estimation algorithm have been compared to those of the manual driving. It has been shown that the vehicle following behavior of the controlled vehicle with the adaptive control algorithm is quite close to that of the human controlled vehicles. Therefore, it can be expected that the more natural and more comfortable vehicle behavior would be achieved by the use of the driver adaptive cruise control algorithm.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.802-803
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• 2011

매입형 영구자석 동기전동기는 강건한 회전자 구조와 릴럭턴스 토크를 사용할 수 있다는 점, 악계자 제어를 하기 쉽다는 점에서 넓은 속도와 토크를 필요로하는 하이브리드 차량이나 전기 차량에 적합한 전동기이다. 영구자석의 감자곡선은 온도에도 의존하기 때문에 온도에 따라 전동기의 자속과 토크가 변하게 된다. 이 논문은 전기 스쿠터에 사용되는 매입형 영구자석 동기전동기의 온도에 따른 지령 전류 보정에 의한 제어법에 대해 연구하였다. 토크 리플을 줄이기 위해서 속도-토크 곡선과 감자곡선을 이용하여 구하였으며, 보정된 지령 전류를 구하기 위해 Lagrange 보간법과 재귀 최소자승(Recursive Least Square : RLS) 법을사용하여 전류맵을 만들었다.이 지령 전류는 토크와 인버터 출력 전류값을 계산하는데 사용된다. 전류맵을 만들기 위해서 측정한 온도는 20, 80, 100도이다. 본 논문에서 제안한 제어법을 이용하여 토크 리플이 줄어듬을 시뮬레이션과 실험을 통해서 확인할 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.3-11
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• 1999

The conventional stereophonic acoustic echo cancellers need two adaptive filters to estimate one channel echo signal. Since the two channel signals are strongly correlated, the ESR of the input signals is considerably increased whatever the input signals may be. This causes the slow convergence of the adaptive filter for echo cancellation. To speed up the convergence, the AP algorithm is frequently used for the stereophonic acoustic echo canceller although there isn't a fast version for 2-channel case. The AP algorithm can be approximated with the Gram-Schmidt orthogonalization and a TDL structure. We propose a two channel algorithm for stereophonic acoustic echo canceller with the approximated AP algorithm.

• Journal of Advanced Navigation Technology
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• v.5 no.2
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• pp.111-122
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• 2001

In this paper, we study the performance of eigencanceller which use a eigenvector and eigenvalue in order to update a weighter vector. Eigencanceller can suppress directional interferences and noise effectively while maintaining specified beam pattern constraints. The constraints and optimal weight vector of eigencanceller vary by using interference and noise or desired signal, interference signal and noise as array input signal. From the analysis results in the steady state, We show that weight vectors in each case are simplified the form of projection equation that belongs to desired subspace orthogonal to interference subspace and eigencanceller has the better performance than RLS method through mathematical analysis and simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.61-64
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS32OC50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator. In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters are determined by the pole-placement method. Performance of self-tuning adaptive controller is illustrated by the simulation and experiment for a SCARA robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.204-210
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• 2005

This paper presents online identification algorithm for TSK fuzzy model. The proposed algorithm identify structure of premise part by using distance, and obtain the parameters of the piecewise linear function consisting consequent part by using recursive least square. Only input space was considered in Most researches on structure identification, but input and output space is considered in the proposed algorithm. By doing so, outliers are excluded in clustering effectively. The existing other algorithm has disadvantage that it is sensitive to noise by using data itself as cluster centers. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. Model can be obtained through one pass and it is not needed to memorize many data in the proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.