• Title, Summary, Keyword: Iterative learning control

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A Study on the Second-order Iterative Learning Control Algorithm with Feedback (궤환을 갖는 2차 반복 학습제어 알고리즘에 관한 연구)

  • Huh, Kyung-Moo
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.5
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    • pp.629-635
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    • 1999
  • A second-order iterative learning control algorithm with feedback is proposed in this paper, in which a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is givenl, and the sufficient condition for the convergence of the algorithm is provided. And it also includes the discussions about the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial. Simulation results show the validity and efficiency of the proposed algorithm.

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Iterative Learning Control for Discrete Time Nonlinear Systems Based on an Objective Function (목적함수를 고려한 이산 비선형 시스템의 반복 학습 제어)

  • Jeong, Gu-Min;Park, Chong-Ho;Jang, Tae-Jeong
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.1
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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.

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Realization of a neural network controller by using iterative learning control (반복학습 제어를 사용한 신경회로망 제어기의 구현)

  • 최종호;장태정;백석찬
    • 제어로봇시스템학회:학술대회논문집
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    • pp.230-235
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    • 1992
  • We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

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Replication of Automotive Vibration Target Signal Using Iterative Learning Control and Stewart Platform with Halbach Magnet Array (반복학습제어와 할바흐 자석 배열 스튜어트 플랫폼을 이용한 차량 진동 신호 재현)

  • Ko, Byeongsik;Kang, SooYoung
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.23 no.5
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    • pp.438-444
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    • 2013
  • This paper presents the replication of a desired vibration response by iterative learning control (ILC) system for a vibration motion replication actuator. The vibration motion replication actuator has parameter uncertainties including system nonlinearity and joint nonlinearity. Vehicle manufacturers worldwide are increasingly relying on road simulation facilities that put simulated loads and stresses on vehicles and subassemblies in order to reduce development time. Road simulation algorithm is the key point of developing road simulation system. With the rapid progress of digital signal processing technology, more complex control algorithms including iterative learning control can be utilized. In this paper, ILC algorithm was utilized to produce simultaneously the six channels of desired responses using the Stewart platform composed of six linear electro-magnetic actuators with Halbach magnet array. The convergence rate and accuracy showed reasonable results to meet the requirement. It shows that the algorithm is acceptable to replicate multi-channel vibration responses.

Model-based iterative learning control with quadratic criterion for linear batch processes (선형 회분식 공정을 위한 이차 성능 지수에 의한 모델 기반 반복 학습 제어)

  • Lee, Kwang-Soon;Kim, Won-Cheol;Lee, Jay-H
    • Journal of Institute of Control, Robotics and Systems
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    • v.2 no.3
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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.

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A Development of Learning Control Method for the Accurate Control of Industrial Robot (산업용 로봇트의 정밀제어를 위한 학습제어 방법의 개발)

  • 원광호;허경무
    • 제어로봇시스템학회:학술대회논문집
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    • pp.346-346
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    • 2000
  • We proposed a method of second-order iterative learning control with feedback, which shows an enhancement of convergence speed and robustness to the disturbances in our previous study. In this paper, we show that the proposed second-order iterative learning control algorithm with feedback is more effective and has better convergence performance than the algorithm without feedback in the case of the existence of initial condition errors. And the convergence woof of the proposed algorithm in the case of the existence of initial condition error is given in detail, and the effectiveness of the Proposed algorithm is shown by simulation results.

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Robust Iterative Learning Control Alorithm

  • Kim, Yong-Tae;Zeungnam Bien
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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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.

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An Iterative Learning Controller Design for Performance Improvement of Multi-Motor System (복수전동기 구동 시스템의 성능 향상을 위한 반복학습제어기 설계)

  • Lee H.H;Kim J.H.
    • Proceedings of the KIPE Conference
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    • pp.584-587
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    • 2003
  • Iterative learning control is an approach to improve the transient response of systems that operate repetitively over a fixed time interval. It is useful for the system where the system output follows the different type input, in case of design or modeling uncertainty In this paper, we introduce the concept of iterative learning control and then apply the learning control algorithm for multi-motor system for performance Improvement.

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Iterative Learning Control for Industrial Robot Manipulators (반복 학습 알고리즘을 이용한 산업용 로봇의 제어)

  • Ha, Tae-Jun;Yeon, Je-Sung;Park, Jong-Hyeon;Son, Seung-Woo;Lee, Sang-Hun
    • Proceedings of the KSME Conference
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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.

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