Disturbance observer‐based adaptive boundary iterative learning control for a rigid‐flexible manipulator with input backlash and endpoint constraint. (3rd August 2020)

• Record Type: Journal Article
• Authors: Zhou, Xingyu; Wang, Haoping; Tian, Yang; Zheng, Gang
• Is Part Of: International journal of adaptive control and signal processing. Volume 34:Number 9(2020)
• Page Start: 1220
• Page End: 1241
• Publication Date: 2020-08-03
• Subjects: adaptive control -- distributed parameter systems -- intelligent control -- learning systems; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3150
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: 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.