• 대한산업공학회지
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• 제23권1호
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• pp.1-22
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• 1997

Learning control refers to 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 a theory of indirect decentralized learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper extends these results to apply to the indirect repetitive control problem in which a periodic (i.e., repetitive) command is given to a control system. Decentralized indirect repetitive control algorithms are presented that have guaranteed convergence to zero tracking error under very general conditions. The original motivation of the repetitive control and learning control fields was learning 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 desired trajectory. Decentralized repetitive control is natural for this application because the feedback control for link rotations is normally implemented in a decentralized manner, treating each link as if it is independent of the other links.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.538-538
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• 2000

This paper presents a learning controller for repetitive gate control of biped robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of teaming control to biped robotic motion is shown via dynamic simulation with 12 dof biped robot.

• 한국군사과학기술학회지
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• 제21권6호
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• pp.850-856
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• 2018

In this paper, a controller based on repetitive learning control is designed to control an unmanned surface vessel with nonlinear characteristics and unknown parameters. First, we define the equations of motion and error system of the unmanned vessel, and then design an repetitive learning controller composed of system error and estimated unknown parameters based on repetitive learning control and adaptive control. The stability of the unmanned vessel model controlled by the designed controller is verified through the analysis of the Lyapunov stability. Simulation shows that the error converges asymptotically to zero with semi-global result, confirming that the unmanned vessel is moving toward a given ideal path, and verifies that the controller is also feasible.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1464-1468
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• 2004

This paper presents a learning controller for repetitive gait control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured according to the walking period through the iterative learning, not calculating the complex dynamics of walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation with 12-DOF biped walking robot.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.594-596
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• 2005

This paper present a learning controller for repetitive gate control of biped robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation and experimental results with 24 DOF biped robot.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.420-426
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• 1998

It is the purpose of this paper to make a preliminary study on the use of repetitive control to improve velocity accuracy by eliminating repetitive disturbances caused by machining inaccuracies of the axis of rotation location. If the control system can be intelligent enough to compensate for such machining errors, then one may be able to improve the accuracy of the velocity control, or alternatively, one may maintain the same accuracy and relax the machining tolerances required. This could decrease cost significantly. Experiments are performed testing repetitive control methods on a constant speed rolling operation testbed. The experimental results show very substantial decreases in the tracking error of the system. Spectral data of the output motion are given to demonstrate the attenuation of the disturbance frequencies and harmonics, related to the bandwidth being used. It is seen that the simplest form of repetitive control which is very easily implemented, can produce striking improvement in control system performance in such belt drive rolling operations, and the learning can be accomplished in a short time.

• International Journal of Precision Engineering and Manufacturing
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• 제4권4호
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• pp.13-18
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• 2003

This paper presents the in-process compensation to control cutter ronout and to improve the machined surface quality. Cutter ronout compensation system consists of the micro-positioning servo system with piezoelectric actuator which is embeded in the sliding table to manipulate radial depth of cut in real-time. Cutting force feedback control was proposed in the angle domain based upon repetitive learning control strategy to eliminate chip load variation in end milling process. Micro-positioning control due to adaptive actuation force response improves the machined surface quality by cutter ronout compensation.

• 전자공학회논문지SC
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• 제40권6호
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• pp.1-7
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• 2003

본 논문은 비선형 시스템에서 시스템의 출력이 주기적인 특징을 가지는 궤적을 따라가도록 하는 슬라이딩 모드 반복 제어기를 소개한다. 본 논문에서 제안하는 제어기는 전체 시스템을 안정화시키며 출력오차를 어떤 범위 안으로 지수적으로 수렴시키는 슬라이딩 모드 제어기와 추적 오차의 수렴을 위해서 사용되는 반복 제어기로 구성되어 있다. 본 논문에서는 제안하는 슬라이딩 모드 제어기는 기존의 방법과는 다르게 제어입력의 크기가 추적오차의 크기에 비례하게 되어 있어서 시스템의 차수를 올리지 않고 정상상태에서의 채터링(chattering) 문제를 크게 개선하는 특징을 가지고 있다.

• 제어로봇시스템학회논문지
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• 제11권4호
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• pp.330-336
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• 2005

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

• 한국정밀공학회지
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• 제19권3호
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• pp.137-143
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• 2002

This paper presents the In-process compensation to control cutter runout and improve the machined surface quality. Cutter runout compensation system consists of the micro-positioning servo system with piezoelectric actuator which is embeded in the sliding table to manipulate radial depth of cut in real-time. Cutting force feedback control was proposed in the angle domain based upon repetitive learning control strategy to eliminate chip load variation in end milling process. Micro-positioning control due to adaptive actuation force response improves the machined surface quality by compensation runout effect induced cutting force variation. This result will provide lots of information to build-up the preciswion machining technology.