• Title, Summary, Keyword: Iterative learning control

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Study on Application of Iterative Learning Control to 2-Mass Resonant System (2관성 공진계에 대한 반복 학습 제어의 응용에 관한 연구)

  • 이학성;문승빈;홍성경
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.1
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    • pp.42-46
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    • 2004
  • A 2-mass resonant system is one that has a flexible coupling between a load and a driving motor. Due to this flexibility, the system often suffers vibration especially when the motor is controlled for higher speed command. In order to suppress such a vibration, an iterative learning control is applied to the 2-mass resonant system in this paper. The motor speed is controlled according to the relation with the load speed. The desired speed trajectories are derived under the condition for no vibration. The simulation result suggests that the proposed method effectively suppresses the vibration even when there exist model uncertainties.

A Study on Implementation of a Real Time Learning Controller for Direct Drive Manipulator (직접 구동형 매니퓰레이터를 위한 학습 제어기의 실시간 구현에 관한 연구)

  • Jeon, Jong-Wook;An, Hyun-Sik;Lim, Mee-Seub;Kim, Kwon-Ho;Kim, Kwang-Bae;Lee, Kwae-Hi
    • Proceedings of the KIEE Conference
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    • pp.369-372
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    • 1993
  • In this thesis, we consider an iterative learning controller to control the continuous trajectory of 2 links direct drive robot manipulator and process computer simulation and real-time experiment. To improve control performance, we adapt an iterative learning control algorithm, drive a sufficient condition for convergence from which is drived extended conventional control algorithm and get better performance by extended learning control algorithm than that by conventional algorithm from simulation results. Also, experimental results show that better performance is taken by extended learning algorithm.

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(Study on an Iterative Learning Control Algorithm robust to the Initialization Error) (초기 오차에 강인한 반복 학습제어 알고리즘에 관한 연구)

  • Heo, Gyeong-Mu;Won, Gwang-Ho
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.39 no.2
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    • pp.85-94
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    • 2002
  • In this paper, we show that the 2nd-order iterative learning control algorithm with CITE is more effective and has better convergence performance than the algorithm without CITE in the case of the existence of initialization errors, for the trajectory-tracking control of dynamic systems with unidentified parameters. In contrast to other known methods, the proposed learning control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a CITE term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances and initialization errors. And the convergence proof of the proposed algorithm in the case of the existence of initialization error is given in detail, and the effectiveness of the proposed algorithm is shown by simulation results.

A Study on Position Control of 2-Mass Resonant System Using Iterative Learning Control (반복 학습 제어를 이용한 2관성 공진계의 위치 제어에 관한 연구)

  • Lee, Hak-Sung;Moon, Seung-Bin
    • Journal of Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.693-698
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    • 2004
  • In this paper, an iterative learning control method is applied to suppress a vibration of a 2-mass system which has a flexible coupling between a load and 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 iterative learning 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 even when there exist model uncertainties. A modification to the learning law is also Presented to suppress undesired effects of an initial position error, The simulation results show the effectiveness of the proposed learning method.

Control and Parameter Estimation of Uncertain Robotic Systems by An Iterative Learning Method (불확실한 로보트 시스템의 제어와 파라미터 추정을 위한 반복학습제어)

  • 국태용;이진수
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.40 no.4
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    • pp.427-438
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    • 1991
  • An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic system is preented. In the learning control structure, the control input converges globally and asymtotically to the desired input as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of the time-duration of trajectories, it may be achieved with system trajectories with small duration. In addition, the proposd learning control schemes are applicable to time-varying parametric systems as well as time-invariant systems, because the parameter estimation is performed at each fixed time along the iteration. In the parameter estimator, the acceleration information as well as the inversion of estimated inertia matrix are not used at all, which makes the proposed learning control schemes more feasible.

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Implementation of a Direct Learning Control Law for the Trajectory Tracking Control of a Robot (로봇의 궤적추종제어를 위한 직접학습 제어법칙의 구현)

  • Kim, Jin-Hyoung;Ahn, Hyun-Sik;Kim, Do-Hyun
    • Proceedings of the KIEE Conference
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    • pp.694-696
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    • 2000
  • In this paper, the Direct Learning Control is applied to robot's trajectory tracking control to solve the problem that lies in the existing Iterative Learning Control(ILC) and the tracking Performance is analyzed and the better approach is searched using computer simulation and experiments. It is assumed that the Direct Learning Control(DLC) is saved onto memory basically after obtaining control input Profiles for several Periodic output trajectories using the ILC. In case the new output trajectory has special relations with the previous output trajectories, there is an advantage that the desired control input profile can be obtained without iterative executions only using the DLC. The robot's tracking control system is comprised of DSP chip. A/D converter, D/A converter and high-speed pulse counter included in the control board and the performance is examined by carrying out the tracking control for the given output trajectory.

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Control and Parameter Estimation of Uncertain Robotic Systems by An Iterative Learning Method (불확실한 로보트 시스템의 제어와 파라미터 추정을 위한 반복학습제어기법)

  • Kuc, Tae-Yong;Lee, Jin-Soo
    • Proceedings of the KIEE Conference
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    • pp.421-424
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    • 1990
  • An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic systems is presented. In the learning control structure, tracking and feedforward input converge globally and asymptotically as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of length of trajectories, it may be achieved with only system trajectories of small duration. In addition, these learning control schemes are expected to be effectively applicable to time-varying parametric systems as well as time-invariant systems, for the parameter estimation is performed at each fixed time along the iteration. Finally, no usage of acceleration signal and no in version of estimated inertia matrix in the parameter estimator makes these learning control schemes more feasible.

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A study on the optimal tracking problems with predefined data by using iterative learning control

  • Le, Dang-Khanh;Le, Dang-Phuong;Nam, Taek-Kun
    • Journal of the Korean Society of Marine Engineering
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    • v.38 no.10
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    • pp.1303-1309
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    • 2014
  • In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.

An iterative learning and adaptive control scheme for a class of uncertain systems

  • Kuc, Tae-Yong;Lee, Jin-S.
    • 제어로봇시스템학회:학술대회논문집
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    • pp.963-968
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    • 1990
  • An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

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Virtual Reference Input Generation Using Direct Learning Control (직접학습제어를 이용한 가상 기준입력 생성)

  • Ahn, Hyun-Sik;Jeong, Gu-Min
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.3
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    • pp.611-614
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    • 2007
  • In this paper, a Direct Learning Control (DLC) method is presented to generate a virtual reference input for linear feedback systems to improve the output tracking performance. The original reference input is effectively modified by the DLC without any iterative learning process. The presented DLC is designed based on the information on the relative degree of a system and previously generated virtual reference inputs. It is illustrated by simulations that the virtual reference input generated by the proposed DLC can achieve high tracking performance, although the reference input cannot be appropriately shaped by using existing DLC methods.