NEURO-FUZZY CONTROLLER FOR CONTROL AND ROBOTICS APPLICATIONS

The purpose of this paper is to develop a neuro-fuzzy controller (NFC) for adaptive tracking in unknown nonlinear dynamic systems, and for on-line computation of inverse kinematic transformations of robot manipulators. The NFC is comprised of a conventional fuzzy logic controller in the feedback configuration and a recurrent neural network in the inverse mode (feedforward) configuration. It is envisaged that the integration of fuzzy logic and neural-network-based controllers will encompass the merits of both the techniques, and thus provide a robust controller. The fuzzy logic controller (FLC), based on fuzzy set theory, provides a means for converting a linguistic control strategy into control actions and thus offering a high level of computation. On the other hand, a recurrent neural network provides low-level computations and embodies salient features such as learning, fault-tolerance, parallelism and generalization. The NFC uses a minimum of fuzzy rules for a given task. The proposed control scheme is implemented for controlling a class of unknown nonlinear dynamic systems, and for computing the inverse kinematic transformations of a two-linked robot.