Abstract: There are many uncertainties in real dynamic system of reconfigurable manipulator that make PID type or traditional control methods unable to realize accurate position control.Thus a neurofuzzy control scheme based on PD control of accurate model was developed to identify and compensate structured and unstructured uncertainties for reconfigurable manipulator.This neurofuzzy compensator infusing fuzzy and neural networks proved to be an effective compensation control strategy for the manipulator trajectory tracking.The dynamics was derived through geometric form NewtonEuler algorithm,and it is characterized by less computation complexity and universal on nlink dynamics equations than other modeling methods for reconfigurable manipulator system. A controller for a Revolute-Revolute-Prismatic(RRP) reconfigurable manipulator was designed and its effectiveness on tracking performance is validated by simulations.

Key words: automatic control technology, neurofuzzy compensator, NewtonEuler algorithm, geometric form, reconfigurable manipulator