Particle swarm optimization (PSO)-based self tuning proportional, integral, derivative (PID) for bearing navigation control system on quadcopter

Abstract

Unmanned Aerial Vehicle (UAV) is an unmanned aircraft that is controlled manually or automatically over long distances. The quadcopter UAV is rapidly developed in recent years due to various purposes. One of the quadcopter navigation system, the UAV moves towards different coordinates by controlling vertical axis rotation angle (yaw) or bearing. In this research, we propose self-tuning proportional integral derivative (PID) control using particle swarm optimization (PSO) method for bearing navigation. Global positioning system (GPS) is used to determine the coordinates of bearing angle to the destination. HMC5883L compass sensor is used to calculate the actual angle of the quadcopter from the earth electromagnetic field. Based on the test results, the quadcopter successfully holds fixed coordinates with settling time at 6.4s and average error after settling time is 5.4o. Based on test result of coordinate changes, the quadcopter is able to reach the aim as fixed coordinates with average error of 7.9o. In the experiment with disturbance, an average offset error of 1.89o and settling time of 4.1 seconds has been achieved. The best PSO self-tuning limits are obtained at Kp = 0.15 to 0.3, Ki = 0.06 to 0.6, and Kd = 0.005 to 0.1. The PSO values used were C1 = 1.5, C2 = 2 and the weight of inertia from 0.7 to 1.2.