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An iterative learning controller with initial state learning
IEEE Transactions on Automatic Control, 1999In iterative learning control (ILC), a common assumption is that the initial states in each repetitive operation should be inside a given ball centred at the desired initial states which may be unknown. This assumption is critical to the stability analysis, and the size of the ball will directly affect the final output trajectory tracking errors.
YangQuan Chen +3 more
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An iteration-domain filter for controlling transient growth in Iterative Learning Control
Proceedings of the 2010 American Control Conference, 2010Transient growth is a problem in Iterative Learning Control (ILC) in which the tracking error temporarily grows very large during the learning process, before converging to a small value. While some ILC algorithms can guarantee monotonic convergence, there are limitations when the model is uncertain.
Qing Liu, Douglas A. Bristow
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On initial conditions in iterative learning control
2006 American Control Conference, 2006Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of iterative learning control methods. In this work we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding learning convergence (or boundedness) property.
Jian-Xin Xu 0001, Rui Yan, YangQuan Chen
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An iterative learning control scheme for manipulators
Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, 2002This paper presents an iterative learning control scheme for high-geared industrial manipulators that perform repeated tasks. The input update law of the iterative learning control is given in the frequency domain, together with a sufficient condition for convergence of the iterative process.
Jung-Ho Moon +2 more
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An iterative learning control of robot manipulators
IEEE Transactions on Robotics and Automation, 1991An iterative learning scheme comprising a unique feedforward learning controller and a linear feedback controller is presented. In the feedback loop, the fixed-gain PD controller provides a stable open neighborhood along a desired trajectory. In the feedforward path, on the other hand, a learning control strategy is exploited to predict the desired ...
KUC, TY, LEE, JS, NAM, KH
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Multiple model iterative learning control
Neurocomputing, 2010Iterative learning controller (ILC), which is based on the model of the system, can give good performance in the steady state if the input is updated from trial to trial. The model, on which the ILC is set up, is not always needed to be exact. But a better model will lead to the deduction of iteration times.
Xiaoli Li 0011, Wen Zhang
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Predictive gradient iterative learning control
2015 54th IEEE Conference on Decision and Control (CDC), 2015Iterative learning control (ILC) is a control design method for high performance trajectory tracking. Most existing results achieve this by learning from information collected over past executions of the task (named trials). This paper proposes a novel gradient based ILC design which updates the control input by learning not only from past trials but ...
Bing Chu +2 more
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An algebraic approach to iterative learning control
Proceedings of the IEEE Internatinal Symposium on Intelligent Control, 2003In this paper discrete-time iterative learning control (ILC) systems are analysed from an algebraic point of view. The algebraic analysis shows that an ILC synthesis problem can be considered as a tracking problem of a multi-channel step-function. Furthermore, the plant to be controlled is a static multivariable plant.
Jari J. Hätönen +2 more
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The influence of disturbances in iterative learning control
Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005., 2005For systems that perform repetitive tasks, a high performance feedforward signal can be derived using Iterative Learning Control (ILC). The feedforward signal is updated through successive iterations. Disturbances present in the control scheme, such as load and measurement disturbances, are also present in the learning process and deteriorate the ...
Roel J. E. Merry +2 more
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1996
Abstract A theory of iterative learning control for refinement of motions of robotic systems is presented, together with simulation results. It is shown that the passivity of such non-linear mechanical systems plays a key role in the ability to acquire a desired and skilled movement through repeated practice.
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Abstract A theory of iterative learning control for refinement of motions of robotic systems is presented, together with simulation results. It is shown that the passivity of such non-linear mechanical systems plays a key role in the ability to acquire a desired and skilled movement through repeated practice.
openaire +1 more source