• 제어로봇시스템학회논문지
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• 제7권1호
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• pp.1147-1154
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• 2001

In this paper, a new iterative learning control scheme for discrete time nonlinear systems is proposed based on an objective function consisting of the output error and input energy. The relationships between the proposed ILC and the optimal control are described. A new input update law is proposed and its convergence is proved under certain conditions. In this proposed update law, the inputs in the whole control horizon are updated at once considered as one large vector. Some illustrative examples are given to show the effectiveness of the proposed method.

• 제어로봇시스템학회논문지
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• 제10권5호
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• pp.395-401
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• 2004

We present an AILC(Adaptive Iterative Learning Control) scheme and a sufficient condition for system parameter identification for uncertain robotic systems that perform the same tasks repetitively. It is guaranteed that the joint velocity and position asymptotically converge to the reference joint velocity and position, respectively. In addition, it is proved that a sufficient condition for parameter identification is the PE(Persistent Excitation) condition on the regressor matrix evaluated at the reference trajectory during the operation period. Since the regressor matrix on the reference trajectory can be easily computed prior to the real robot operation, the proposed algorithm provides a useful method to verify whether the parameter error converges to zero or not.

• 한국재료학회:학술대회논문집
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• 한국재료학회 2003년도 추계학술발표강연 및 논문개요집
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• pp.188-188
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• 2003

본 연구에서는 Pd에 의한 MILC(Metal induced lateral crystallization)시 수소 플라즈마 처리의 영향에 따른 결정화 양상 및 TFT에 미치는 효과에 대해 고찰하였다. 현재까지 Pd에 의한 MILC에 의해 Channel이 제작된 TFT는 Transfer curve 특성을 보이지 않는것으로 보고되어 있다. 이는 바늘 모양의 성장으로 인해 결정질 부분이 다 채워지지 않고 비정질 부분이 존재하고, 결정질 내에 쌍정 결함이 존재하기 때문인 것으로 알려져 있다. 본 실험에서는 수소 플라즈마 처리 시 Pd에 의한 MILC 양상이 수소 플라즈마 처리를 하지 않은 경우와는 달리 측면 결정화 양상이 바늘 모양의 성장 모습을 보이지 않고, 결정질 부분이 다 채워진 평탄한 모양의 성장 모습을 보였다.

• 전기학회논문지
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• 제57권1호
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• pp.117-120
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• 2008

This paper investigates the convergence condition of ADILC(iterative learning control with advanced output data) for nonminimum phase systems. ADILC has simple learning structure including both minimum phase and nonminimum phase systems. However, for nonminimum phase systems, the overall time horizon must be considered in input update law. This makes the dimension of convergence condition matrix large. In this paper, a new sufficient condition is proposed to satisfy the convergence condition. Also, it has been shown that this sufficient condition can be satisfied although it is not full impulse response.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.319-323
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• 1994

In this paper, we discuss a design method of iterative learning control systems for parabolic linear distributed parameter systems(DPSs). First, we discuss some aspects of boundary control of the DPS, and then propose to employ the Karhunen-Loeve procedure to reduce the infinite dimensional problem to a low-order finite dimensional problem. An iterative learning control(ILC) for non-square transfer function matrix is introduced finally for the reduced order system.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.56-61
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• 2003

해양경계 획선은 해양 국가들 사이의 협상 중 매우 중요한 쟁점이었다. 각각의 협상의 문제는 공평의 법적 원칙이 엄격하게' 적용되는 국제적 경계를 결정하는 것이다. 하나의 공식이 각국의 경제적, 지형적 그리고 주위요소와의 전략적 경우에 모두 적용될 수 없기 때문에 어떠한 특별한 방법이 정해지지 않았다. 그러나 균등한 경계를 얻기 위한 몇 가지 지침이 1953년부터 International Law Commission(ILC)에 의해 제안되었다 이 지침들 중에 하나는 등거리 원칙(the principle of equidistant)에 의거한 일반적인 규칙을 사용한다. 이 등거리 원칙은 경계상의 모든 점들은 해안의 한 폭에서 측정된 기준선(baseline)상의 최근접점에서 항상 등거리에 있어야 한다. 이 원칙을 구현하기 위한 여러 가지 기하학적 방법이 적용되어왔다. 본 연구에서는 등거리 원칙을 기본으로 하는 알고리즘 중 광범위하게 사용되고 있는 Two-Point 알고리즘과 Three-Point 알고리즘을 정립한 후, 이 알고리즘 결과를 확인할 수 있는 해양경계 획선 프로그램을 웹으로 구현하였다.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.225-227
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• 2006

This note investigates the convergence condition of ADILC (iterative learning control with advanced output data) for nonminimum phase systems. ADILC has simple learning structure including both minimum phase and nonminimum phase systems. However, for nonminimum phase systems, the overall time horizon must be considered in input update law. This makes the dimension of convergence condition matrix large. In this paper, a new sufficient condition is proposed to satisfy the convergence condition. Also, it has been shown that this sufficient condition can be satisfied although it is not full impulse response.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.745-750
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• 2008

Uncertain dynamic parameters and joint flexibility have been problem to control robot manipulator precisely. Hence, even if the controller tracks the desired trajectory well with the feedback of the motor encoders, it is hard to achieve the desired behavior at the end-effector. In this paper, robot trajectory is taught by a general heuristic iterative learning control (ILC) algorithm in order to reduce tracking error of the tool center point (TCP) and the results of tracking with 6 DOF industrial robot manipulator are presented. The performance is verified based on ISO 9283.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.71-77
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• 1995

In this paper are proposed robust iterative learning control(ILC) algorithms for both linear continuous time-invariant system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithm.