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• W2110644382 abstract "SUMMARY This paper presents a model‐based nonlinear iterative learning control (NILC) for nonlinear multiple‐input and multiple‐output mechanical systems of robotic manipulators. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory‐tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. Both standard and bounded‐error learning control laws with feedback controllers attached are considered. The NILC synthesis is based on a dynamic model of a six degrees of freedom robotic manipulator. The dynamic model includes viscous and Coulomb friction and input generalized torques are bounded. With respect to the bounded‐error and standard learning processes applied to a virtual PUMA 560 robot (Unimation, Inc. Danburry, CT, USA), simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control. Copyright © 2013 John Wiley & Sons, Ltd." @default.
• W2110644382 created "2016-06-24" @default.
• W2110644382 creator A5034113052 @default.
• W2110644382 date "2013-12-08" @default.
• W2110644382 modified "2023-10-16" @default.
• W2110644382 title "Iterative learning control for robotic manipulators: A bounded-error algorithm" @default.
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• W2110644382 doi "https://doi.org/10.1002/acs.2454" @default.
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