Altitude Control of Quadrotor Based on Metaheuristic Methods

Quadrotor, which is used in many fields and is still a challenge to control, has a complex kinematic and dynamic system, and its flight performance depends on many variables that need to be controlled simultaneously. In this study, the effective determination of PID parameters for altitude control of quadrotors, which presents a complex control problem, has been tested comparatively with innovative metaheuristic approaches. Among the strong metaheuristic algorithms, Crow Search Algorithm (CSA), Particle Swarm Optimization Algorithm (PSO), Golden Jackal Optimization Algorithm (GJO), and Jellyfish Search Algorithm (JSA) were comparatively analyzed for the determination of PID parameters. The parameters obtained with CSA caused the minimum steady-state error with the value of 6.9580e-04 in the closed-loop control system. A minimum overshoot was also obtained with the parameters optimized with CSA. When these results are evaluated, it can be said that CSA performs better than other altitude control algorithms, considering the quadrotor's stable and accurate positioning performance.