A neurofuzzy controller for a single link flexible manipulator

This paper presents an adaptive neurofuzzy controller for tip position tracking control of a single link flexible manipulator. The controller has a self-organizing fuzzy neural structure in which fuzzy rules are generated during the control process using an online learning algorithm. In order to demonstrate the superior performance of the proposed controller, the results are compared with those obtained by using the proportional-derivative (PD) and neural network controllers. Moreover, since the proposed controller requires no a priori knowledge about the system, it can efficiently cope with the uncertainties such as payload mass variations.