• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.101-105
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• 1989

The iterative learning controller makes the system output follow the desired output over a finite time interval through iterating trials. In this paper, first we discuss that the design problem of learning controller is originally the design problem of the inverse model. Then we show that the tracking error which is the difference between the desired output and the system output is reduced monotonically by properly modeled inverse system if the magnitude of the learning operator being introduced is bounded within the unit circle in complex domain. Also it would be shown that the conventional learning control method is a kind of extremely simplified inverse model learning control method of the objective controlled system. Hence this control method can be considered as a generalization of the conventional learning control method. The more a designer model the objective controlled system precisely, the better the performance of the approximated inverse model learning controller would be. Finally we compare the performance of the conventional learning control method with that of the approximated inverse model learning control method by computer simulation.

• International Journal of Precision Engineering and Manufacturing
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• 제3권1호
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• pp.33-42
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• 2002

Recently, various experiments to apply reinforcement learning method to the self-learning intelligent control of continuous dynamic system have been reported in the machine learning related research community. The reports have produced mixed results of some successes and some failures, and show that the success of reinforcement learning method in application to the intelligent control of continuous control systems depends on the ability to combine proper function approximation method with temporal difference methods such as Q-learning and value iteration. One of the difficulties in using function approximation method in connection with temporal difference method is the absence of guarantee for the convergence of the algorithm. This paper provides a proof of convergence of a particular function approximation method based on \"barycentric interpolator\" which is known to be computationally more efficient than multilinear interpolation .

• 말소리와 음성과학
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• 제3권3호
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• pp.91-98
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• 2011

In this paper, a multi-channel speech enhancement method using the linearly constrained minimum variance (LCMV) algorithm and a variable learning rate control is proposed. To control the learning rate for adaptive filters of the LCMV algorithm, the direction of arrival (DOA) is measured for each short-time input signal and the likelihood function of the target speech presence is estimated to control the filter learning rate. Using the likelihood measure, the learning rate is increased during the pure noise interval and decreased during the target speech interval. To optimize the parameter of the mapping function between the likelihood value and the corresponding learning rate, an exhaustive search is performed using the Bark's scale distortion (BSD) as the performance index. Experimental results show that the proposed algorithm outperforms the conventional LCMV with fixed learning rate in the BSD by around 1.5 dB.

• 한국지능시스템학회논문지
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• 제8권3호
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• pp.9-15
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• 1998

본 논문은 뉴로제어 및 반복학습 제어기법에 기반한 미지의 비선형시스템의 적응학습제어 방법을 제안한다. 제안된 제어 시스템에서 반복학습제어기는 새로운 기준 궤적에 대해 시스템의 출력이 원하는 궤적으로 정확히 수렴하도록 하는 적응과 단기간 제어정보를 기억하는 기능을 수행한다. 상대차수만 알고 있는 미지 시스템에 대한 박복학습 법칙이 학습이득은 신경회로망을 이용하여 추정된다. 반복학습제어기에 의해 습득된 제어정보는 장기메모리에 기반한 앞먹임 뉴로제어기로 이전되어 누적기억됨으로써 과거에 겸험된 기준 궤적에 대해서는 신속하게 추종할 수 있도록 한다. 2자유도 매니퓰레이터에 적용하여 제안된 기법의 타당성을 검증한다.

• 대한가정학회지
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• 제50권8호
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• pp.125-135
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• 2012

The purpose of this study is to examine the mediating effects of self-determined motivation between mother's psychological control and self-directed learning ability in children. The participants were 457 sixth-grade elementary students in the Gyung-gi province. They completed questionnaires that included the Self-Directed Learning Readiness Scale, K-SPQ-A, Psychological Control Scale. Descriptive statistics and Pearson's product correlation coefficients were obtained using SPSS (version 18.0), and tests of the mediation were performed using SEM with AMOS (version 18.0). The major findings of this study were as follows that significant correlations among maternal psychological control, self-determined motivation and self-directed learning exist. Also a mother's psychological control was negatively related to a child's self-directed learning. The relationship between maternal psychological control and a child's self directed learning was fully mediated by self determined motivation. These results suggested that high maternal psychological control was negatively affected that development of self-determined motivation and self-directed learning.

• 한국공간구조학회논문집
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• 제21권1호
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.248-255
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• 2000

Whereas the linear PID controller is widely used for control of industrial servo systems a high precision positioning system is not easy to control only with the PID controller due to uncertain nonlinear dynamics such as friction backlash etc. As a viable means to overcome the difficulty a learning control scheme is proposed in this paper that is simple and straightforward to implement. The proposed learning controller takes full advantage of current feedback capability of the inner-loop of the control system in that electrical motor dynamics as the well as mechanical part of X-Y positioning system is included in the learning control scheme, The experimental results are given to demonstrate its feasibility and effectiveness in terms of convergence precision of tracking and robustness in comparison with the conventional control method.

• 한국산업융합학회 논문집
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• 제2권1호
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1705-1714
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• 2018

This paper develops a novel controller called iterative learning sliding mode (ILSM) to control linear and nonlinear fractional-order systems. This control applies a combination structures of continuous and discontinuous controller, conducts the system output to the desired output and achieve better control performance. This controller is designed in the way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order systems. The proposed controller unlike the conventional iterative learning control for fractional systems does not need to apply direct control input to output of the system. It is shown that the controller perform well in partial and complete observable conditions. Simulation results demonstrate very good performance of the iterative learning sliding mode controller for achieving the desired control objective by increasing the number of iterations in the control loop.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.303-306
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• 1996

We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.