• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.40-47
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work the authors presented an iterative precision of linear decentralized learning control based on p-integrated teaming method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the loaming control field was learning in robots doing repetitive tasks such as on a]1 assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.211-217
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the teaming control field was teaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• ICROS
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• v.10 no.2
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• pp.19-34
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• 2004

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.5
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• pp.422-428
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• 1988

An iterative learning control system is a control system which makes system outputs follow desired outputs by iterating its trials over a finite time interval. In a discrete time system, we proposed one method in which present control inputs can be obtained by a linear combination of the input sequence and time-shifted error sequence at previous trial. In contrast with a continous time learning control system which needs differential opreration of an error signal, the time shift operation of the error sequence is simpler in a computer control system and its effectiveness is shown by a simulation.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.126-130
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• 2007

This paper presents a method to design a repetitive controller that is specified in the specified trajectory for the repetitive works. With the single-model design approach, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed single-model design method produces a repetitive controller in a single nominal model of the system.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.1
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• pp.10-15
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• 2002

It is presented to extended to an interaction matrix formulation to the problem of system and disturbance identification for a plant that is corrupted by both process and output disturbances. With only an assumed upper bound on the order of the system and an assumed upper bound on the number of disturbance frequencies, it is shown that both the disturbance-free model and disturbance effect can be recovered exactly from disturbance-corrupted input-output data without direct measurement of the periodic disturbances. The rich information returned by the identification can be used by an iterative learning or repetitive control system to eliminate unwanted periodic disturbances. Those can be helped to apply to the multiple dynamic systems for precision quality assurance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.137-140
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• 2003

2관성 공진계는 전동기와 부하 사이에 탄성이 있는 동력 전달 체계를 포함하는 시스템으로 고속 제어시 진동이 발생된다. 이와 같은 2관성 공진계에 대해 궁극적인 출력이 되는 부하의 위치 및 속도는 측정하기가 어렵다. 이에 기존의 연구들은 추정기등을 이용하여 부하의 속도를 추정하여 이를 제어에 활용하는 방식으로 구성되어진다. 그러나 이와 같이 추정기를 사용하기 위해서는 정확한 모델이 필요하다. 본 논문에서는 다소 부정확 모델에 대해서도 매우 정교한 제어 성능을 갖는 반복 학습 제어를 이용하여 2관성 공진계의 위치 제어를 시도하고자 한다. 제시된 방법은 2관성 공진계에 대한 모델링이 정확하지 않더라도 진동 없이 정확한 위치 제어가 가능하다.

• Proceedings of the KAIS Fall Conference
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• 2000.10a
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• pp.218-222
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• 2000

본 논문에서는 피드백 사용형 2차 반복 학습제어 방법이 수렴 성능의 향상과 외란에 대한 강인성 향상에 덧붙여 학습제어의 피드백 항을 이용함으로써 초기 조건 오차가 있음에도 불구하고 이를 극복할 뿐만 아니라 기존의 알고리즘보다 더 빠른 수렴 능력이 있음을 확인한다. 또한 불안정한 결과를 낳는 높은 학습 제어 게인의 경우에도 피드백 항을 추가한 본 학습제어 방법에 의해 안정화됨으로써, 빠른 응답 특성과 강인성 향상을 가져올 수 있음을 보인다. 그리고 본 알고리즘을 선형 시변 연속 시스템이 적용된 모델 시스템을 설계하여 이를 통한 시뮬레이션 결과로서 초기 조건 오차의 극복 능력이 뛰어남을 확인하고 시스템의 안정화와 강인성 향상에 기여함을 확인한다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.2
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• pp.19-25
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• 2003

For a class of multi-input multi-output nonlinear systems which perform a given task repetitively, an extended type of a direct leaning control (DLC) is proposed using the information on the (vector) relative degree of a multi-input multi-output system. Existing DLC methods are observed to be applied to a limited class of systems with the relative degree one and a new DLC law is suggested which can be applied to systems having higher relative degree. Using the proposed control law, the control input corresponding to the new desired output trajectory is synthesized directly based on the control inputs obtained from the learning process for other output trajectories. To show the validity and the performance of the proposed DLC, simulations are performed for trajectory tracking control of a two-axis SCARA robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.175-179
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• 2003

Convergence condition determines performance of iterative learning control (ILC), for example, convergence speed, remaining error, etc. Hence, the performance can be elevated and a feasible set of learning controllers grows if a less conservative condition is obtained. In the frequency domain, the $H_{\infty}$ norm of the transfer function between consecutive errors has been currently used to test convergence of a learning system. However, even if the convergence condition based on the $H_{\infty}$ norm has a clear property about monotonic convergence, it has a few drawbacks, especially in MIMO plants. In this paper, the relation between the condition and the monotonicity of convergence is clarified and a modified convergence condition is found out using a frequency domain Lyapunov equation, which supersedes the conventional one in the frequency domain.