<\/p>\n
Modeling and Control of a 3DOF Robot
\nManipulator Using Artificial Fuzzy-Immune
\nFOPID Controller
\nWOROD ADRIS SHUTNAN 1, NORA AHMED MOHAMMED1, FARIS ASAAD ABDULMUNEM1,
\nALI HAMZAH NAJIM2, MUSTAFA YAHYA HASSAN3, NAGLAA F. SOLIMAN 4,
\nAND ABEER D. ALGARNI4
\n1Department of Electronic and Communications Engineering, College of Engineering, University of Al-Qadisiyah, Al-Diwaniyah 58002, Iraq
\n2Department of Computer Technical Engineering, Imam Al-Kadhum College (IKC), Al-Diwaniyah, Iraq
\n3Department of Computer Science and Information Technology, University of Al-Qadisiya, Al-Diwaniyah 58002, Iraq
\n4Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671,
\nSaudi Arabia
\nCorresponding author: Worod Adris Shutnan (worod.adris@qu.edu.iq)
\nThis work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R51),
\nPrincess Nourah bint Abdulrahman University, Saudi Arabia.
\nABSTRACT A robotic manipulator is a highly nonlinear, coupled system with many inputs and outputs
\n(MIMO). These days, with the development of technology, using robots has become very common in
\nvarious fields. It is difficult for the control experts to create an effective controller for this system, so it is
\npreferable to design a non-linear controller that gives an accurate, more efficient controller and good results
\nin robustness and uncertainty. Two distinct controllers for a 3-DoF robotic manipulator are developed and
\nevaluated in this research\u2019s context of Industry. During the investigation, two primary control strategies are
\nintroduced. One of the first is a fuzzy-immune PID controller that employs joint position error as inputs.
\nA second type is a fuzzy-immune FOPID controller. The PID controller is a particular case of FOPID
\ncontroller when \u03bb = \u03bc = 1. The research underscores the significance of precise parameter calibration
\nand data acquisition in optimizing the performance of a control system. The parameters of controllers
\nare tuned using the clonal selection algorithm. The controllers\u2019 efficacy was verified through quantitative
\nanalysis that employed performance indices, including mean square error (MSE). The robot manipulator is
\nconstructed with MATLAB. The proposed technique, which combines intelligent control methods, offers
\na promising hybrid control design. Our software implementation has demonstrated that the fuzzy immune
\nFOPID controller is more efficient in terms of reduced tracking error of the manipulator for its dynamic
\ncontrol in the joint space than other controllers typically used in practice. The positioning control system
\nutilizes Fuzzy Immune PID and FOPID control techniques. All controllers were designed using MATLAB<\/p>\n