PID Tuning and Stability Analysis of Hybrid Controller for Robotic Arm Using ZN, PSO, ACO, and GA

Abstract

This paper aims to tune and implement the Hybrid controller for the motion control of robot manipulators using different soft computing techniques. The hybrid controller is the combination of the PID (Proportional, Integral, and Derivative) and overwhelming controller. The different soft computing techniques such as Ziegler-Nichols (ZN), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) are utilized to tune the parameters of the PID. The simulation environment MATLAB has been used to obtain the results for different soft computing techniques, which are compared to obtain the best-tuned parameters. The results of the comparison have indicated that the GA performs exceptionally better than other optimization techniques. The Routh Hurwitz criterion is also used to analyze and validate the stability and efficacy of the proposed work. The tuned PID parameters are applied to a single-arm robot manipulator, which is interfaced by MATLAB Simulink. The results obtained through simulation have revealed that the proposed method is superior to traditional methods.