Performance of Metaheuristic Optimization Algorithms based on Swarm Intelligence in Attitude and Altitude Control of Unmanned Aerial Vehicle for Path Following

Abstract

Nowadays, it is very important for the success of the determined missions or operations that the Unmanned Aerial Vehicles (UAVs), which are used extensively in the performance of many civil and military tasks, follow the predetermined path with high accuracy at the determined altitude. The fact that the UAV performs its mission by adhering to the predetermined height and path enables the UAV to spend less energy and therefore fly for a longer time. Many traditional control algorithms, especially Proportional-Integral-Derivative (PID), are used in the attitude and altitude control of UAV for path following. Unlike other studies, in this study, metaheuristic optimization algorithms based on swarm intelligence estimate the parameters of the control algorithm proposed for UAV. Using meta-heuristic optimization algorithms such as Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO), both attitude and altitude control of the quadrotor have been performed for path following in routes with different geometries such as rectangle, circle, and lemniscate. The performance of each control algorithm in the study for the specified routes has been tested and the test results obtained have been compared with each other. Considering the features such as simplicity, flexibility, ability to search randomly and avoiding local optima, a new control algorithm whose K P , K I , and K D parameter values optimized by HHO, have been proposed for UAV’s attitude and altitude control.