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Disturbance observer‐based adaptive boundary iterative learning control for a rigid‐flexible manipulator with input backlash and endpoint constraint

Xingyu Zhou 1 Haoping Wang 1 Yang Tian 1 Gang Zheng 2
2 DEFROST - Deformable Robots Simulation Team
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : In this article, an observer‐based adaptive boundary iterative learning control law is developed for a class of two‐link rigid‐flexible manipulator with input backlash, the unknown external disturbance, and the endpoint constraint. To tackle the backlash nonlinearities and ensure the vibration suppression, the disturbance observers based upon the iterative learning conception are considered in the adaptive boundary control design. A barrier Lyapunov function is incorporated with boundary control law to restrict the endpoint state. Based on the defined barrier composite energy function, the tracking angle error convergence of the rigid part is guaranteed, and the vibrations of the flexible part are suppressed through the rigorous analysis. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed control.
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https://hal.inria.fr/hal-03087577
Contributor : Gang Zheng Connect in order to contact the contributor
Submitted on : Wednesday, December 23, 2020 - 11:30:27 PM
Last modification on : Friday, January 21, 2022 - 3:12:22 AM

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Xingyu Zhou, Haoping Wang, Yang Tian, Gang Zheng. Disturbance observer‐based adaptive boundary iterative learning control for a rigid‐flexible manipulator with input backlash and endpoint constraint. International Journal of Adaptive Control and Signal Processing, Wiley, 2020, 34 (9), pp.1220-1241. ⟨10.1002/acs.3150⟩. ⟨hal-03087577⟩

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