Comparative performance evaluation of PID and fuzzy PID controller using genetic algorithm for a robotic manipulator system

The robotic manipulator system has demonstrated its efficacy in a variety of fields. As a result, it is essential to design an efficient controller for managing the end position of a robotic arm. Due to the rising nonlinearity and complexity of robotic manipulator systems, a typical proportional-integral-derivative controller has proven ineffective. In recent years, intelligent techniques such as fuzzy logic, neural networks, and optimization algorithms have emerged as a potent tool for managing extremely complex nonlinear processes with uncertain dynamics. In this study, a PID Controller and a Fuzzy Proportional-Integral-Derivative controller with parameters generated by a Genetic Algorithm using Absolute Error Integral as the goal function are created. In this paper, simulated output results of plants controlled by a Fuzzy ProportionalIntegralDerivative controller are presented, and the superiority of the implemented controller is demonstrated through a comparison with a conventional Proportional-Integral-Derivative controller using the Genetic Algorithm optimization technique.