Modeling and Control of a 3DOF Robot
Manipulator Using Artificial Fuzzy-Immune
FOPID Controller
WOROD ADRIS SHUTNAN 1, NORA AHMED MOHAMMED1, FARIS ASAAD ABDULMUNEM1,
ALI HAMZAH NAJIM2, MUSTAFA YAHYA HASSAN3, NAGLAA F. SOLIMAN 4,
AND ABEER D. ALGARNI4
1Department of Electronic and Communications Engineering, College of Engineering, University of Al-Qadisiyah, Al-Diwaniyah 58002, Iraq
2Department of Computer Technical Engineering, Imam Al-Kadhum College (IKC), Al-Diwaniyah, Iraq
3Department of Computer Science and Information Technology, University of Al-Qadisiya, Al-Diwaniyah 58002, Iraq
4Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671,
Saudi Arabia
Corresponding author: Worod Adris Shutnan (worod.adris@qu.edu.iq)
This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R51),
Princess Nourah bint Abdulrahman University, Saudi Arabia.
ABSTRACT A robotic manipulator is a highly nonlinear, coupled system with many inputs and outputs
(MIMO). These days, with the development of technology, using robots has become very common in
various fields. It is difficult for the control experts to create an effective controller for this system, so it is
preferable to design a non-linear controller that gives an accurate, more efficient controller and good results
in robustness and uncertainty. Two distinct controllers for a 3-DoF robotic manipulator are developed and
evaluated in this research’s context of Industry. During the investigation, two primary control strategies are
introduced. One of the first is a fuzzy-immune PID controller that employs joint position error as inputs.
A second type is a fuzzy-immune FOPID controller. The PID controller is a particular case of FOPID
controller when λ = μ = 1. The research underscores the significance of precise parameter calibration
and data acquisition in optimizing the performance of a control system. The parameters of controllers
are tuned using the clonal selection algorithm. The controllers’ efficacy was verified through quantitative
analysis that employed performance indices, including mean square error (MSE). The robot manipulator is
constructed with MATLAB. The proposed technique, which combines intelligent control methods, offers
a promising hybrid control design. Our software implementation has demonstrated that the fuzzy immune
FOPID controller is more efficient in terms of reduced tracking error of the manipulator for its dynamic
control in the joint space than other controllers typically used in practice. The positioning control system
utilizes Fuzzy Immune PID and FOPID control techniques. All controllers were designed using MATLAB

modeling-and-control-of-a-3-dof-robot-manipulator-using-artificial-fuzzy-immune-fopid-controller