2022/02/25 by Leonardo Colombo, Leonardo J. Colombo, Manuela Gamonal Fernández +5
Computer Science · Engineering · Mathematics · #Advanced Control Systems Optimization #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #cs.SY #eess.SY #electronic engineering #information engineering #math.OC
paper · pdf · doi:10.48550/arxiv.2202.12736
arxiv created 2022/02/25 · openalex publication_date 2022/02/25 · arxiv updated 2022/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.