Robust Control of a Two-Link Flexible Manipulator with Quasi-Static Deflection Compensation Using Neural Networks

Authors:
Y. Li;G. Liu;T. Hong;K. Liu
Affiliations:
Department of Control Science and Engineering, Jilin University, Changchun, China 130025;Department of Aerospace Engineering, Ryerson University, Toronto, Canada MSB 2K3;Department of Control Science and Engineering, Jilin University, Changchun, China 130025;Department of Control Science and Engineering, Jilin University, Changchun, China 130025
Venue:
Journal of Intelligent and Robotic Systems
Year:
2005

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Abstract

A robust control method of a two-link flexible manipulator with neural networks based quasi-static distortion compensation is proposed and experimentally investigated. The dynamics equation of the flexible manipulator is divided into a slow subsystem and a fast subsystem based on the assumed mode method and singular perturbation theory. A decomposition based robust controller is proposed with respect to the slow subsystem, and H 驴 control is applied to the fast subsystem. The overall closed-loop control is determined by the composite algorithm that combines the two control laws. Furthermore, a neural network compensation scheme is also integrated into the control system to compensate for quasi-static deflection. The proposed control method has been implemented on a two-link flexible manipulator for precise end-tip tracking control. Experimental results are presented in this paper along with concluding remarks.