Adaptive-fuzzy logic control of robot manipulators

This paper presents a methodology for the design of fuzzy logic controllers (FLC) that guarantees prescribed performance for a general robotic system. It is shown that the proposed online learning algorithm learns the stabilizing membership functions (MFs) online from initial MFs that are selected using simple design criteria. From a practical standpoint, the controller structure leads to efficient implementation and fills a void that existed in the lack of repeatable design methodologies for FLC implementation