Optimization of Quadcopter PID Controller Gains using Ant Colony Optimization and Genetic Algorithm

Abstract

Proportional Integral Derivative (PID) controllers stand as the cornerstone in most robotic applications due to their inherent simplicity and practicality.However, the manual tuning of these controllers often proves to be an ineffective approach to obtaining optimal gains for specific operational requirements.Consequently, there arises a demand for computational intelligence, leveraging meta-heuristic algorithms to systematically determine the most suitable combination of gains.Among the plethora of meta-heuristic algorithms, Genetic Algorithm has gained prominence for its efficacy in intelligent problem-solving.Similarly, Ant Colony Optimization Algorithm is recognized for its effectiveness.This paper conducts a comparative analysis of the performance of PID controllers when tuned using these algorithms.The investigation involves a simulation using MATLAB 2023a, with documentation of results presented.Through this analysis, the paper aims to provide insight into how ACO and GA can be used to tune the PID controller for quadcopters.