Optimization of PID controller by genetic algorithm experiment on delta robot

Abstract

This study aims (i) to design optimal controllers for a Delta robot, and (ii) to experiment with controlling the robot’s end-effector tracking reference trajectories. Delta robot is a parallel robot that has a fairly wide range of uses in industries. There exist many methods for tracking control of the Delta robot, and PID controller is a popular choice because of its low cost of design and experiment. However, arm parameters such as weight, joint, and friction can be changed and affect the operation of the whole system, where PID controllers no longer maintaining control quality. Therefore, this paper presents the analysis, comparison, and evaluation of using the Genetic Algorithm (GA) for self-tuning the PID controller based on criteria of the absolute value of error (IAE). On the other hand, this paper also presents experimental steps to control the Delta robot. Results with GA-PID controller indicate that robot responses archive settling time of about 0.5 (s), and the overshoot is only 3.14 %. Experimental results also show that the proposed algorithm is stable and has a fast response in controlling the motion of the Delta robot.