Cervantes Rojas, Jorge Said (UMI-LAFMIA 3175 CNRS), MUÑOZ PALACIOS, FILIBERTO (UNIVERSIDAD POLITÉCNICA DE PACHUCA), Gonzalez-Hernandez, Ivan (Cinvestav - IPN), Salazar, Sergio (UMI LAFMIA CINVESTAV), Chairez, Isaac (UPIBI - IPN), Lozano, Rogelio (University of Technology of Compiègne)

Neuro-Fuzzy Controller for Attitude-Tracking Stabilization of a Multi-Rotor Unmanned Aerial System

Scheduled for presentation during the "UAS Control - III" (FrC5), Friday, June 16, 2017, 15:40−16:00, San Marco Island

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

This paper deals with developing an automatic controller that solves the attitude stabilization for a Quadrotor unmanned aerial system (UAS). The controller used a simultaneous strategy of estimation and compensation of uncertainties as well as disturbances. The approach consisted of integrating a neuro-fuzzy system that implemented a set of differential neural networks (DNNs) as consequence section of Takagi-Sugeno (T-S) fuzzy inference. The combination of these two strategies applied on a Quadrotor UAS has the main purpose of forcing a hover flight while the tracking desired angular positions are attained. The control method identified the unknown nonlinearities and bounded external disturbances firstly. This information served to compensate the uncertain section of the Quadrotor dynamics. An additional section in the controller design enforces the stabilization of the tracking error with respect to a given reference trajectory. The control design methodology supported on the Lyapunov stability theory and guaranteed ultimate boundedness of the identification and tracking errors. Academic simulation tests confirmed the superior performance of the proposed algorithm based on the combination of DNNs and T-S techniques.