• Title/Summary/Keyword: Iterative Learning Control

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Advanced Control Techniques for Batch Processes Based on Iterative Learning Control Methods (반복학습제어를 기반으로 한 회분공정의 고급제어기법)

  • Lee, Kwang Soon
    • Korean Chemical Engineering Research
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    • v.44 no.5
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    • pp.425-434
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    • 2006
  • The operability and productivity of continuous processes, especially in petrochemical industries have made remarkable improvement during the past twenty years through advanced process control (APC) typified by model-based predictive control. On the other hand, APC have not been actively practiced in industrial batch processes typified by batch polymerization reactors. Perhaps the main cause for this has been the lack of reliable batch process APC techniques that can overcome the unique problems in industrial batch processes. Recently, some noteworthy progress is being made in this area. New high-performance batch process control techniques that can accommodate and also overcome the unique problems of industrial batch processes have been proposed on the basis of iterative learning control (ILC). In this review paper, recent advancement in the batch process APC techniques are presented, with a particular focus on the variations of the so called Q-ILC method, with the hope that they are widely practiced in different industrial batch processes and enhance their operations.

Convergence Conditions of Iterative Learning Control in the Frequency Domain (주파수 영역에서 반복 학습 제어의 수렴 조건)

  • Doh, Tae-Yong;Moon, Jung-Ho
    • 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.

A computed torque method incorporating an iterative learning scheme

  • Nam, Kwanghee
    • 제어로봇시스템학회:학술대회논문집
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    • 1989.10a
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    • pp.1097-1112
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    • 1989
  • An iterative learning control scheme is incorporated to the computed torque method as a means to enhance the accuracy and the flexibility. A learning rule is constructed by utilizing a gradient descent algorithm and data compressing techniques are illustrated. Computer simulation results show a good performance of the scheme under a relatively high speed and a heavy payload condition.

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A Dual-Stage Servo System for an NFR Disk Drive using Iterative Learning Control (반복 학습 제어를 이용한 NFR 디스크 드라이브의 2단 서보 시스템)

  • 문정호;도태용
    • Journal of Institute of Control, Robotics and Systems
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    • v.9 no.4
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    • pp.277-283
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    • 2003
  • Recently, near-field recording (NFR) disk drive schemes have been proposed with a view to increasing recording densities of hard disk drives. Compared with hard disk drives. NFR disk drives have narrower track pitches and are exposed to more severe periodic disturbances resulting from eccentric rotation of the disk. It is difficult to meet servo system design specifications for NFR disk drives with conventional VCM actuators in that the servo system for an NFR disk drive generally requires a feater gain and higher bandwidth. To tackle the problem various dual-stage actuator systems composed of a microactuator mounted on top of a conventional VCM actuator have been proposed. This article deals with the problem of designing a tracking servo system far an NFR disk drive adopting a dual-stage actuator. We summarize design constraints pertaining to the dual-stage servo system and present a new servo scheme using iterative teaming control. We design feedback compensators and an iterative teaming controller for a target plant and verify the validity of the proposed control scheme through a computer simulation.

Identification and Multivariable Iterative Learning Control of an RTP Process for Maximum Uniformity of Wafer Temperature

  • Cho, Moon-Ki;Lee, Yong-Hee;Joo, Sang-Rae;Lee, Kwang-S.
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.2606-2611
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    • 2003
  • Comprehensive study on the control system design for a RTP process has been conducted. The purpose of the control system is to maintain maximum temperature uniformity across the silicon wafer achieving precise tracking for various reference trajectories. The study has been carried out in two stages: thermal balance modeling on the basis of a semi-empirical radiation model, and optimal iterative learning controller design on the basis of a linear state space model. First, we found through steady state radiation modeling that the fourth power of wafer temperatures, lamp powers, and the fourth power of chamber wall temperature are related by an emissivity-independent linear equation. Next, for control of the MIMO system, a state space modeland LQG-based two-stage batch control technique was derived and employed to reduce the heavy computational demand in the original two-stage batch control technique. By accommodating the first result, a linear state space model for the controller design was identified between the lamp powers and the fourth power of wafer temperatures as inputs and outputs, respectively. The control system was applied to an experimental RTP equipment. As a consequence, great uniformity improvement could be attained over the entire time horizon compared to the original multi-loop PID control. In addition, controller implementation was standardized and facilitated by completely eliminating the tedious and lengthy control tuning trial.

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Iterative learning control for discrete-time feedback systems and its applicationto a direct drive SCARA robot (이산시간 궤환 시스템에 대한 반복학습제어 및 직접구동형 SCARA 로보트에의 응용)

  • Yeo, Seong-Won;Kim, Jae-Oh;Hwang, Gun;Kim, Sung-Hyun;Kim, Do-Hyun;Ahn, Hyun-Sik
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.34S no.7
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    • pp.56-65
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    • 1997
  • In this paper, we propose a reference input odification-type iterative learning control law for a class of discrete-time nonlinear systems and prove the convergence of the output error. We can get the high-precision in case of the trajectroy control when the proposed control law is properly combined with a feedback controller, and we can easily implement the learning control law compared to the control input modification-type learning control law. To show the validity and the convergence perfodrmance of the proposed control law, we perform experimentations on the trajectroy control and rejection of periodic disturbance for a 2-axis SCARA-type direct drive robot.

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Finite-horizon Tracking Control for Repetitive Systems with Uncertain Initial Condition (불확실한 초기치를 갖는 반복시스템에 대한 유한구간 추종제어)

  • Choi, Yun-Jong;Yun, Sung-Wook;Lee, Chang-Hee;Cho, Jae-Young;Park, Poo-Gyeon
    • Proceedings of the KIEE Conference
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    • 2007.10a
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    • pp.297-298
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    • 2007
  • Repetitive systems stand for a kind of systems that perform a simple task on a fixed pattern repetitively and are widely spread in industrial fields. Hence, those systems have been of much interests by many researchers, especially in the field of iterative learning control (ILC). In this paper, we propose a finite-horizon tracking control scheme for linear time-varying repetitive systems with uncertain initial conditions. The scheme is derived both analytically and numerically for state-feedback systems and only numerically for output-feedback systems. Then, it is extended to stable systems with input constraints. All numerical schemes are developed in the forms of linear matrix inequalities. A distinguished feature of the proposed scheme from the existing iterative learning control is that the scheme guarantees the tracking performance exactly even under uncertain initial conditions. The simulation results demonstrate the good performance of the proposed scheme.

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An Efficient Method to Determine the Phase Current Commands of SR Motors for Minimum Torque Ripples (SR 모터의 토크리플을 최소화하는 상전류명령 결정 방법)

  • Kim, Chang-Hwan
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.49 no.4
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    • pp.78-89
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    • 2012
  • The generated torque of a switched reluctance(SR) motor is highly nonlinear, which makes it difficult to determine the reference current commands for minimum torque ripples. In this paper, we present a computationally simple and efficient method to minimize torque ripples of SR motors based on iterative learning control. The reference current command of each phase minimizing torque ripples is identified in 2-dimensional look-up table form. Our learning control algorithm does not require the torque model, so our method is not affected by model errors and hence is very accurate. In order to justify our work, we present some computer simulation results.

Simplification and Scaling of Iterative Learning Control Command (반복학습제어 명령의 간단화와 스케일링)

  • Chae, Hui-Chang;Lee, Sang-Hoon;Park, Myung-Kwan;Suh, Il-Hong
    • Proceedings of the KIEE Conference
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    • 2003.07d
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    • pp.2390-2392
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    • 2003
  • ILC(Iterative Learning Control: 이하 ILC)는 현재 기계, 전기, 화학 등 많은 분야에 널리 적용되고 있다. ILC는 특히 반복적인 trajectory tracking Control 문제에 아주 효과적인 방법 중의 하나이다. 하지만 ILC는 메모리 기반의 scheme로서 trajectory tracking을 위해서는 많은 메모리를 요구하게 된다. 한편, 자세한 관찰에 의하면 인간의 팔, 다리 등의 관절의 움직임은 아주 정확하지가 않다. 이러한 사실로 미루어 인간이 정화한 모션을 취하는데 드는 비용을 줄이고자 모션 명령을 간단히 한다는 가정을 추론 해 낼 수 있다. 이러한 가정에 기초하여 우리는 ILC 명령을 간단히 하기 위해서 약간의 trajectory tracking의 정확성을 회생하는 메커니즘을 제안한다. 간단해진 ILC 명령은 적은 메모리 공간에 저장될 것이다. 또한, 로봇의 trajectory tracking을 위한 기존의 방법들은 아주 복잡할 뿐만 아니라 하나의 task의 수행만이 가능할 뿐 어떤 일반화의 방법도 제시하지 못하고 있다. 그래서 본 논문에서는 ILC 명령의 scaling에 대한 메커니즘을 제공하여 하나의 trajectory에 대해서 비슷한 모양이지만 다른 크기와 속도를 가지는 trajectory를 구현 할 수 있도록 하였다.

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A Study on Convergence Property of Iterative Learning Control (반복 학습 제어의 수렴 특성에 관한 연구)

  • Park, Kwang-Hyun;Bien, Z. Zenn
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.38 no.4
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    • pp.11-19
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    • 2001
  • In this paper, we study the convergence property of iterative learning control (ILC). First, we present a new method to prove the convergence of ILC using sup-norm. Then, we propose a new type of ILC algorithm adopting intervalized learning scheme and show that the monotone convergence of the output error can be obtained for a given time interval when the proposed ILC algorithm is applied to a class of linear dynamic systems. We also show that the divided time interval is affected from the learning gain and that convergence speed of the proposed learning scheme can be increased by choosing the appropriate learning gain. To show the effectiveness of the proposed algorithm, two numerical examples are given.

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