Iterative learning control: Convergence, robustness and by Yangquan Chen PhD, Changyun Wen PhD (eds.)

By Yangquan Chen PhD, Changyun Wen PhD (eds.)

This publication offers readers with a entire insurance of iterative studying regulate. The ebook can be utilized as a textual content or reference for a path at graduate point and is usually compatible for self-study and for industry-oriented classes of continuous education.
Ranging from aerodynamic curve id robotics to practical neuromuscular stimulation, Iterative studying regulate (ILC), all started within the early 80s, is located to have huge functions in perform. as a rule, a procedure below keep an eye on can have uncertainties in its dynamic version and its atmosphere. One appealing element in ILC lies within the utilisation of the procedure repetitiveness to minimize such uncertainties and in flip to enhance the keep an eye on functionality by means of working the method time and again. This monograph emphasises either theoretical and useful points of ILC. It presents a few contemporary advancements in ILC convergence and robustness research. The booklet additionally considers matters in ILC layout. a number of functional purposes are awarded to demonstrate the effectiveness of ILC. The utilized examples supplied during this monograph are quite invaluable to readers who desire to capitalise the procedure repetitiveness to enhance procedure keep an eye on performance.

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25 Fig. 3. Tracking performances comparison for N = 1, 2, 3, with random re-initialization errors. 3 D i s c u s s i o n s The above two examples verified t h a t 9 time delays in state variables do not affect the ILC convergence performance significantly; 9 a high-order ILC scheme can be better t h a n a first-order one. While the main concern in this chapter is the convergence analysis of a high-order ILC algorithm, the design issues are still open. 1 is only a guide line. 1, the design t a s k highly depends on the amount of knowledge of the system as well as the performance index imposed.

This has been demonstrated in Fig. 2. 01s and a set of typical learning gains (M = 1; Q1 = 200, Q0 = 25) are shown in Fig. 3 for every ILC iteration. 50), the errors are shown in Fig. 4. For different sampling 52 ILC for Uncertain Nonlinear Discrete-time Systems using CITE periods the effects of prediction errors are summarized in Figs. 50) respectively. From these calculations, it is quite clear that, in this simulation study, the prediction errors are not critical to the ILC convergence. This observation will also apply to many other applications.

While the main concern in this chapter is the convergence analysis of a high-order ILC algorithm, the design issues are still open. 1 is only a guide line. 1, the design t a s k highly depends on the amount of knowledge of the system as well as the performance index imposed. 3. C h a p t e r 8 emphasizes this ILC design issue and an interesting application with detailed design steps is given in [234]. 5 Conclusion The convergence of a PID-type high-order ILC algorithm for uncertain nonlinear systems with state delays is shown to be guaranteed if the reinitialization errors, the uncertainties or disturbances are bounded.

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