• Title/Summary/Keyword: Iterative Learning Control

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Flexible Iterative Learning Control Based Expert System and Its Application

  • Zuojun, Liu;Zhihu, Liu;Linan, Zu;Peng, Yang
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.9 no.3
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    • pp.185-190
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    • 2009
  • A scheme of expert system based on iterative learning control is proposed. Iterative learning control can obtain control experience from the historical data to build the knowledge base of expert system. Considering some uncertain factors, a flexible measure is adopted in iterative learning control (ILC). Simulation proves the feasibility and effect of the air conditioning control expert system based on flexible iterative learning control (F-ILC). Finally, a feedback compensation unit is incorporated against irregular heavy disturbance.

Torque Ripple Minimization of PMSM Using Parameter Optimization Based Iterative Learning Control

  • Xia, Changliang;Deng, Weitao;Shi, Tingna;Yan, Yan
    • Journal of Electrical Engineering and Technology
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    • v.11 no.2
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    • pp.425-436
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    • 2016
  • In this paper, a parameter optimization based iterative learning control strategy is presented for permanent magnet synchronous motor control. This paper analyzes the mechanism of iterative learning control suppressing PMSM torque ripple and discusses the impact of controller parameters on steady-state and dynamic performance of the system. Based on the analysis, an optimization problem is constructed, and the expression of the optimal controller parameter is obtained to adjust the controller parameter online. Experimental research is carried out on a 5.2kW PMSM. The results show that the parameter optimization based iterative learning control proposed in this paper achieves lower torque ripple during steady-state operation and short regulating time of dynamic response, thus satisfying the demands for both steady state and dynamic performance of the speed regulating system.

Robust tuning of quadratic criterion-based iterative learning control for linear batch system

  • Kim, Won-Cheol;Lee, Kwang-Soon
    • 제어로봇시스템학회:학술대회논문집
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    • pp.303-306
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    • 1996
  • We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.

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Estimation of learning gain in iterative learning control using neural networks

  • Choi, Jin-Young;Park, Hyun-Joo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.91-94
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    • 1996
  • This paper presents an approach to estimation of learning gain in iterative learning control for discrete-time affine nonlinear systems. In iterative learning control, to determine learning gain satisfying the convergence condition, we have to know the system model. In the proposed method, the input-output equation of a system is identified by neural network refered to as Piecewise Linearly Trained Network (PLTN). Then from the input-output equation, the learning gain in iterative learning law is estimated. The validity of our method is demonstrated by simulations.

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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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Control of Wafer Temperature Uniformity in Rapid Thermal Processing using an Optimal Iterative teaming Control Technique (최적 반복 학습 제어기법을 이용한 RTP의 웨이퍼 온도균일제어)

  • 이진호;진인식;이광순;최진훈
    • 제어로봇시스템학회:학술대회논문집
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    • pp.358-358
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    • 2000
  • An iterative learning control technique based on a linear quadratic optimal criterion is proposed for temperature uniformity control of a silicon wafer in rapid thermal processing.

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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 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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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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