Fuzzy logic-based self-tuning autopilots for trajectory tracking of a low-cost quadcopter: A comparative study

Abstract

In this work, we develop self-tuning PD-fuzzy autopilots for trajectory tracking of a low-cost Parrot AR.Drone2 quadcopter. We first recall the mathematical model of the system in terms of its multi-input, multi-output (MIMO) transfer function model acquired via system identification technique. Accordingly, we design three self-tuning autopilots by means of fuzzy inference systems to control the position of the drone in 3D space. This research serves as a preliminary study in our design process to investigate the feasibility of our fuzzy self-tuning autopilot before we can implement it into practice. We perform a systematic comparative study to highlight the effectiveness of our control algorithm with respect to fixed-gain autopilot as well as fuzzy logic controller.