Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty

TL;DR

This paper introduces a learning-based control method using Gaussian processes for a hexarotor UAV that can reconfigure its rotors to handle failures, ensuring reliable tracking despite uncertainties.

Contribution

It presents a novel Gaussian process-based fault-tolerant control approach that estimates and compensates for model uncertainties after rotor failures.

Findings

Probabilistic bounded tracking error guaranteed.

Effective recovery from rotor failures demonstrated.

Experimental validation on hexarotor UAV.

Abstract

In this paper we present a learning-based tracking controller based on Gaussian processes (GP) for a fault-tolerant hexarotor in a recovery maneuver. In particular, to estimate certain uncertainties that appear in a hexacopter vehicle with the ability to reconfigure its rotors to compensate for failures. The rotors reconfiguration introduces disturbances that make the dynamic model of the vehicle differ from the nominal model. The control algorithm is designed to learn and compensate the amount of modeling uncertainties after a failure in the control allocation reconfiguration by using GP as a learning-based model for the predictions. In particular the presented approach guarantees a probabilistic bounded tracking error with high probability. The performance of the learning-based fault-tolerant controller is evaluated through experimental tests with an hexarotor UAV.