Trajectory tracking double two-loop adaptive neural network control for a Quadrotor

Ivan Lopez-Sanchez, Ricardo Pérez-Alcocer*, Javier Moreno-Valenzuela

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In this paper, the development and experimental validation of a novel double two-loop nonlinear controller based on adaptive neural networks for a quadrotor are presented. The proposed controller has a two-loop structure: an outer loop for position control and an inner loop for attitude control. Similarly, both position and orientation controllers also have a two-loop design with an adaptive neural network in each inner loop. The output weight matrices of the neural networks are updated online through adaptation laws obtained from a rigorous error convergence analysis. Thus, a training stage is unnecessary prior to the neural network implementation. Additionally, an integral action is included in the controller to cope with constant disturbances. The error convergence analysis guarantees the achievement of the trajectory tracking task and the boundedness of the output weight matrix estimation errors. The proposed scheme is designed such that an accurate knowledge of the quadrotor parameters is not needed. A comparison against the proposed controller and two other well-known schemes is presented. The obtained results showed the functionality of the proposed controller and demonstrated robustness to parametric uncertainty.

Original languageEnglish
Pages (from-to)3770-3799
Number of pages30
JournalJournal of the Franklin Institute
Volume360
Issue number5
DOIs
StatePublished - Mar 2023

Bibliographical note

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© 2023 The Franklin Institute

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