Adaptive intelligent control of flexible link robot arm

In this study, adaptive control of flexible link model which is non-minimum phase and single-input, multiple-output (SIMO) is presented. The controllers designed aim to control the hub position in a way that attenuates the tip deflections with less energy consumption. Methods used to design the under actuated controller are WRBF network and neuro-fuzzy network and are compared to LQR and non-adaptive fuzzy controller. Learning method performed for adaptive schemes is emotional. Simulation results show the effectiveness of the designed controllers and reduction of energy demand in intelligent adaptive controllers.