Fuzzy Learning based Adaptive Control for a Two-Link Flexible Manipulator

This paper presents a new fuzzy learning based adaptive controller (FLAC) for tip position control of a flexible manipulator (FLM) when subjected to handle different payloads. The proposed adaptive control strategy consists of an fuzzy learning mechanism and a reference model. The effectiveness of the proposed control algorithm is then verified by comparing its performance with a Lyapunov based direct adaptive controller (DAC). The metrics of the controller performance are chosen as tip trajectory tracking accuracy and fast suppression of tip deflection. From the simulation and experimental results it is observed that the proposed adaptive controller FLAC exhibits superior performance compared to the DAC.