• Title, Summary, Keyword: Iterative learning control

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A study on Indirect Adaptive Decentralized Learning Control of the Vertical Multiple Dynamic System

  • Lee, Soo-Cheol;Park, Seok-Sun;Lee, Jeh-Won
    • International Journal of Precision Engineering and Manufacturing
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    • v.7 no.1
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    • pp.62-66
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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 learning control field was learning in robots doing repetitive tasks such as an assembly line works. 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. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.

A Study on the Properness Constraint on Iterative Learning Controllers (반복 학습 제어기의 properness 제한에 관한 연구)

  • Moon, Jung-Ho;Doh, Tae-Yong
    • Journal of Korean Institute of Intelligent Systems
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    • v.12 no.5
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    • pp.393-396
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    • 2002
  • This note investigates the necessity of properness constraint on iterative learning controllers from the viewpoint of the initial condition problem. It is shown that unless the iterative learning controller is proper, the teaming control input may grow unboundedly and thus not be feasible in practice, though the convergence of tracking error is theoretically guaranteed. In addition, this note analyzes the effects of initial condition misalignment in the iterative learning control system on the control input and convergence property.

Model predictive control combined with iterative learning control for nonlinear batch processes

  • Lee, Kwang-Soon;Kim, Won-Cheol;Lee, Jay H.
    • 제어로봇시스템학회:학술대회논문집
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    • pp.299-302
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    • 1996
  • A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

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Application of Iterative Learning Control to 2-Mass Resonant System with Initial Position Error (위치 오차를 갖는 2관성 공진계에 대한 반복학습 제어의 적용에 관한 연구)

  • Lee, Hak-Seong
    • Proceedings of the KIEE Conference
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    • pp.307-310
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    • 2003
  • In this paper, an iterative learning control method is applied to suppress the vibration of a 2-mass system which has a flexible coupling between a load an a motor. More specifically, conditions for the load speed without vibration are derived based on the steady-state condition. And the desired motor position trajectory is synthesized based on the relation between the load and motor speed. Finally, a PD-type learning iterative control law is applied for the desired motor position trajectory. Since the learning law applied for the desired trajectory guarantees the perfect tracking performance, the resulting load speed shows no vibration. In order to handle the initial position error, the PD-type learning law is changed to PID-type and a weight function is added to suppress the residual vibration caused by the initial error. The simulation results show the effectiveness of the proposed learning method.

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An iterative learning approach to error compensation of position sensors for servo motors

  • Han, Seok-Hee;Ha, In-Joong;Ha, Tae-Kyoon;Huh, Heon;Ko, Myoung-Sam
    • 제어로봇시스템학회:학술대회논문집
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    • pp.534-540
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    • 1993
  • In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algrithms need a special perfect position sensor or a priori information about error sources, while ours does not. To our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function inself rather than its special time-trajectory and does not request the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.

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Iterative learning control of nonlinear systems with consideration on input magnitude (입력의 크기를 고려한 비선형 시스템의 반복학습 제어)

  • Choi, Chong-Ho;Jang, Tae-Jeong
    • Journal of Institute of Control, Robotics and Systems
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    • v.2 no.3
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    • pp.165-173
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    • 1996
  • It is not desirable to have too large control input in control systems, because there are usually a limitation for the input magnitude and cost for the input energy. Previous papers in the iterative learning control did not considered on these points. In this paper, an iterative learning control method is proposed for a class of nonlinear systems with consideration on input magnitude by adopting a concept of cost function consisting of the output error and the input magnitude in quadratic form. We proposed a new input update law with an input penalty function. If we choose a reasonable input penalty function, the two control objectives, good command following and small input energy, can be achieved. The characteristics of the proposed method are shown in the simulation examples.

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Adaptive Learning Control fo rUnknown Monlinear Systems by Combining Neuro Control and Iterative Learning Control (뉴로제어 및 반복학습제어 기법을 결합한 미지 비선형시스템의 적응학습제어)

  • 최진영;박현주
    • Journal of Korean Institute of Intelligent Systems
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    • v.8 no.3
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    • pp.9-15
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    • 1998
  • This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

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FUZZY SLIDING MODE ITERATIVE LEARNING CONTROL Of A MANIPULATOR

  • Park, Jae-Sam
    • Proceedings of the IEEK Conference
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    • pp.1483-1486
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    • 2002
  • In this paper, a new scheme of iterative loaming control of a robot manipulator is presented. The proposed method uses a fuzzy sliding mode controller(FSMC), which is designed based on the similarity between the fuzzy logic control(FLC) and the sliding mode control(SMC), for the feedback. With this, the proposed method makes possible fDr fast iteration and has advantages that no linear approximation is used for the derivation of the learning law or in the stability proof Full proof of the convergence of the fuzzy sliding base learning scheme Is given.

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On iterative learning control for some distributed parameter system

  • Kim, Won-Cheol;Lee, Kwang-Soon;Kim, Arkadii-V.
    • 제어로봇시스템학회:학술대회논문집
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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.

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PID Type Iterative Learning Control with Optimal Gains

  • Madady, Ali
    • International Journal of Control, Automation, and Systems
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    • v.6 no.2
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    • pp.194-203
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    • 2008
  • Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.