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

In this brief, we present a learning-based tracking controller based on Gaussian processes (GPs) for a fault-tolerant hexarotor in a recovery maneuver. In particular, we use GPs to estimate certain uncertainties that appear in a hexacopter vehicle with the ability to reconfigure its rotors to compen...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2024-03, Vol.32 (2), p.1-8
Main Authors: Colombo, Leonardo J., Giribet, Juan I.
Format: Article
Language:English
Subjects:
Algorithms
Autonomous aerial vehicles
Bayesian learning
Control theory
Controllers
data-driven control
Dynamic models
Fault tolerance
Fault tolerant systems
fault-tolerant control
Gaussian process
Learning
Mathematical models
multirotor vehicles
Pulse width modulation
Reconfiguration
Rotors
Statistical analysis
Tracking control
Tracking errors
Uncertainty
Unmanned aerial vehicles
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Summary:In this brief, we present a learning-based tracking controller based on Gaussian processes (GPs) for a fault-tolerant hexarotor in a recovery maneuver. In particular, we use GPs to estimate certain uncertainties that appear in a hexacopter vehicle with the ability to reconfigure its rotors to compensate for failures. The rotor's 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 for 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 by experimental tests with a hexarotor unmanned aerial vehicle (UAV).
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2023.3318855