Design of Mixed Fuzzy-GA Controller For SCARA Type Robot

This paper presents a mixed fuzzy-GA controller (MFGAC) for trajectory tracking of an industrial selective compliance assembly robot arm (SCARA), which is one of the most employed manipulators in industrial environments. In this robot nonlinear effects due to centrifugal, coriolis and internal forces are more important than friction and gravity forces, unlike most industrial robots. The control procedure of MFGAC is consisting of a mixed fuzzy controller which is optimized by genetic algorithm. In this work we first design a traditional fuzzy controller (TFC) from the viewpoint of a single-input single-output (SISO) system for controlling each degree of freedom of the robot. Then, an appropriate coupling fuzzy controller is also designed according to the characteristics of robot's dynamic coupling and incorporated into a TFC. After that by using genetic algorithm we tune and optimize the membership functions and scaling factors of designed fuzzy controller. This control strategy can not only simplify the implementation problem of fuzzy control, but also improve control performance