Fuzzy Adaptive Neurons Applied to the Identification of Parameters and Trajectory Tracking Control of a Multi-Rotor Unmanned Aerial Vehicle Based on Experimental Aerodynamic Data

The propulsion subsystem of multi-rotor Unmanned Aerial Vehicles (UAV) is one of the most complex due to the aerodynamic, aero-elastic and electromechanical elements it comprises. Therefore, accurate models of this subsystem can be difficult to work with. Therefore, simplified models are normally used for the design of control and navigation algorithms. Considering this, the effectiveness of these algorithms is heavily dependent on the identification process used for the estimation of the parameters of the simplified propulsion model. On the other hand, the novel method of fuzzy adaptive neurons (FANs) have interesting characteristics that make them attractive for applications in which a fast response and good precision are required. In this article, the identification of the parameters of the propulsion system and the trajectory tracking of a multi-rotor UAV using FANs is explored. The efficient learning algorithm of the FANs is applied to identify the parameters of a simplified model of the propulsion system and to the self-tuning proportional integral derivative (PID) controllers of the trajectory tracking system. The proposed simplified model with the identified parameters is tested with experimental data obtained with low speed wind tunnels. The proposed PID controllers with self-tuning gains defined by the algorithm of the FANs for trajectory tracking system, are verified with simulations in MATLAB/Simulink (R) environment. The results showed that the parameter identification and trajectory tracking with PID controllers with self-tuning gains defined by the algorithm of the FANs, are suitable for estimating the parameters of the simplified model and track the trajectory with better error reduction than a classical PID controller.