Abstract

To meet the COVID-19 challenges, the fifth digital industrial wave demands an artificially-intelligent control system advances in selective compliance assembly robot arm (SCARA). Since SCARA has an inherently nonlinear dynamic system and friction rejection, this work develops an artificial intelligent (AI) fuzzy-based nonlinear control algorithm (impedance strategy). Consequently, a generalized dynamic five degree of freedom SCARA (5-Dof SCARA) has mathematically been modelled. These nonlinear controllers have been realized as AI-based fuzzy architectures for the position part from the impedance controller; fuzzy logic proportional derivative (PD) control-type one and Fuzzy logic proportional derivative integral (PID) control type two. These AI architectures regulate the position tracking error of the end effectors' forces. That has been tested using procedures based on half- elliptic and full-elliptic trajectories. Comparatively, the test results are tabulated with related existing published results. Both AI-based controllers have been efficiently dealt with robot nonlinear models and friction rejection. However, the proposed Fuzzy logic PD type one controller has been produced a less-optimum response with the inherent system nonlinearities. Thus, an AI fuzzy-based control algorithm has to be optimally developed to resolve the SCARA sluggishness in achieving tasks. Consequently, a fuzzy logic control (FLC) algorithm of a three-dimension membership function (fuzzy type- 2) has been designed to improve the position response of the SCARA redundant robot end-effectors; Fuzzy logic PID type two-controller. The third dimension is dedicated to overcoming uncertain limits in the nonlinear system that leads to a more stable response in the robot end-effector with no oscillation and zero error. The test results of the AI fuzzy-based PID nonlinear controller has been successfully manifested the superiority in performance to be properly updated in the space shuttle remote manipulator system. Where the large enchantment in position trajectory reached by FLC type-1 PD controller is 93.166% and 25% in X and Y axis respectively. The large enhancement by the FLC type-2 PID controller is 95.501% and 31.250% in X and Y-axis respectively.

Keyword

5-Dof SCARA robot, PID, PD, Impedance controller, Position controller, Fuzzy type-1 controller, Fuzzy type-2 controller, AI fuzzy nonlinear controller, Space shuttle.

Cite this article

Ghintab SS, Karam ZA, Hasan S

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