Highly-Error Enhanced Smartly-Algorithmic Structured Impedance Fuzzy Controllers for A SCARA Redundant Manipulator

Abstract

Living in the COVID-19 era, the various advanced industrial automated production lines demand anAI and Machine learning research in Robotic. Thus, an AI fuzzy-based control algorithms need to be developed.Consequently, a generalized Selective Compliance Assembly Robot Arm (5-Dof SCARA) dynamic model hasbeen derived. This 5-Dof SCARA model is inherently nonlinear, hence, to be controlled by a smart nonlinearcontroller of fuzzy type at the highest error enhancement. The smart nonlinear fuzzy controllers have beendeveloped in unified FLC type-1 and type-2 architectures. Firstly, an impedance controller deals with the endeffectors forces and its position tracking error. Secondly, a position controller is FLC type-1 PD and FLC type-2PID. The two controllers have been tested using half- elliptic and full-elliptic trajectory, then, compared to existrelated works. Accordingly, the obtained results of the smart controllers have a maximum percentage PD ofenhancements in comparison with previous works by the position responses. The FLC type-1 impedancecontroller has accomplished a 93.273% and 33.333% error enhancement for the position response of the X andY axis respectively. Comparably, higher error enhancement has been obtained using the FLC type-2 PIDimpedance controller of a 95.574% and 38.887% with the same axes. Hence, the designed smart controllershave a potential future application in advanced and critical trends.