Modeling and Control of a 3DOF Robot Manipulator Using Artificial Fuzzy-Immune FOPID Controller

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda =\mu =1$ </tex-math></inline-formula>. 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.