An Optimal Fuzzy Self-Tuning PID Controller for Robot Manipulators via Genetic Algorithm

This paper deals with the problem of optimizing a fuzzy self-tuning PID controller for robot manipulators. Fuzzy PID controllers have been developed and applied in many fields in the last fifteen years. However, there is no systematic method to design Membership Functions (MFs) for these controllers. We propose a simple method based on Genetic Algorithms (GA) to find optimal input and output MFs of a fuzzy selftuning PID controller. The stability via Lyapunov theory for the closed loop control system is also analyzed and shown that is asymptotically stable for a class of gain matrices depending on the manipulator states. To show the usefulness of the proposed approach, simulation results using a two degree of freedom robot arm are presented.