Transition control of a tail-sitter UAV using recurrent neural networks

TL;DR

This paper introduces an RNN-based control system for stabilizing tail-sitter UAVs during transition maneuvers, effectively estimating aerodynamic forces and ensuring smooth hover-cruise transitions through simulation results.

Contribution

It develops a novel RNN-based controller that estimates nonlinear aerodynamic effects for tail-sitter UAV transition stabilization, combining it with feedback linearization.

Findings

Convergence of linear velocities during transition

Successful stabilization of pitch angle

Effective simulation results for hover-cruise transition

Abstract

This paper presents the implementation of a Recurrent Neural Network (RNN) based-controller for the stabilization of the flight transition maneuver (hover-cruise and vice versa) of a tail-sitter UAV. The control strategy is based on attitude and velocity stabilization. For that aim, the RNN is used for the estimation of high nonlinear aerodynamic terms during the transition stage. Then, this estimate is used together with a feedback linearization technique for stabilizing the entire system. Results show convergence of linear velocities and the pitch angle during the transition maneuver. To analyze the performance of our proposed control strategy, we present simulations for the transition from hover to cruise and vice versa.