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Varying-order iterative learning control against perturbed initial conditions. (English) Zbl 1202.93053
Summary: A homing mechanism is required for repositioning as a system performs tasks repeatedly. By examining the effect of poor repositioning on the tracking performance of iterative learning control, this paper develops a varying-order learning approach for the performance improvement. Through varying-order learning, the resultant system output trajectory is ensured to follow a given trajectory with a lowered error bound, in comparison with the conventional fixed-order method. A discrete-time initial rectifying action is introduced in the formed varying-order learning algorithm, and a sufficient condition for convergence is derived. An implementable scheme is presented based on the proposed approach, and illustrated by numerical results of two examples of robotic manipulators.

MSC:
93C10 Nonlinear systems in control theory
93C55 Discrete-time control/observation systems
93C85 Automated systems (robots, etc.) in control theory
68T05 Learning and adaptive systems in artificial intelligence
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