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Dynamic neuro-fuzzy adaptive control for flexible-link manipulators based on dynamic inversion

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4 Author(s)
Fu-Chun Sun ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China ; Ling-Bo Zhang ; Hua-Ping Liu ; Zeng-qi Sun

A dynamic neuro-fuzzy (DNF) adaptive control system is presented in this paper for the trajectory tracking of a flexible-link manipulator with poorly known dynamics, where the robot tip vibration is measured by position sensitive detectors (PSDs). Based on the singular perturbation method and two time-scale decompositions, the robot dynamics is approximated by a slow subsystem of an equivalent rigid arm and a fast subsystem of flexible mode. Then, the DNF adaptive controller based on dynamic inversion is designed for the tracking control of the equivalent rigid arm, while a fuzzy proportional derivative (PD) type controller is used to stabilize the elastic dynamics using PSDs measurement feedback. The NF learning algorithm and the stability proof of the closed-loop system are given, and an upper bound for the singular perturbation parameter is also obtained. Finally, the real-time experiment is conducted to show the viability and effectiveness of the proposed control approach.

Published in:

Intelligent Control. 2003 IEEE International Symposium on

Date of Conference:

8-8 Oct. 2003

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