Titlebook: Real-time Iterative Learning Control; Design and Applicati Jian-Xin Xu,Sanjib K. Panda,Tong Heng Lee Book 2009 Springer-Verlag London 2009

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Introduction to ILC: Concepts, Schematics, and Implementation,ications – the global Lipschitz continuity condition and identical initialization condition, and explore possible extensions. Finally we discuss three practical issues encountered in real-time ILC implementation – repetitive control tasks, robustness and filter design, as well as the sampling effect.

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ILC for Precision Servo with Input Non-linearities: Application to a Piezo Actuator,eadzone, and show that ILC algorithms remain effective in general for systems with input non-smooth non-linearities that could be unknown and state dependent. In addition, in this chapter we will also briefly discuss the input saturation and input backlash.

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ILC with Robust Smith Compensator: Application to a Furnace Reactor,obust performance design problem) for the iterative process to converge for all admissible plant uncertainties. In addition, the iterative learning controller under plant uncertainty is designed. An illustrative example demonstrating the main result is presented.

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Calibration of Micro-robot Inverse Kinematics Using Iterative Learning Approach,bration sample, it is possible to estimate all unknown parameters non-linear in the inverse kinematics. A multi-link closedchain roboticmanipulator is utilized as an example to verify thismethod. Sensitivity analysis with respect to the calibration precision is also conducted.

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1430-9491 their own plants based on many practical examples.Gives the.Real-time Iterative Learning Control. demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC d

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Robust Optimal ILC Design for Precision Servo: Application to an XY Table,ementation. We further elaborate on the issue of robust optimal design, which seeks the fastest learning convergence under the worst-case parametric uncertainties. The experimental results show that the high-precision tracking performance is achieved via iterative learning, despite the existence of miscellaneous non-linear and uncertain factors.

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Introduction,nced by making full use of the process repeatability. Roboticmanipulator control was the first successful application [4]. It was reported that the tracking error can be reduced to 1/1000 by 12 iterations with only simple changes of the reference signals [77].

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