Robust control of a two-link flexible manipulator with quasi-static deflection compensation using neural networks
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 infinity 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.
robust control flexible manipulator neural network quasi-static compensation