<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>An integrated approach to the fault-tolerant control (FTC) of a quadcopter unmanned aerial vehicle (UAV) with incipient actuator faults is presented. The framework is comprised of a radial basis function neural network (RBFNN) fault detection and diagnosis (FDD) module and a reconfigurable flight controller (RFC) based on the extremum seeking control approach. The dynamics of a quadcopter subject to incipient actuator faults are estimated using a nonlinear identification method comprising a continuous forward algorithm (CFA) and a modified golden section search (GSS) one. A time-difference-of-arrival (TDOA) method and the post-fault system estimates are used within the FDD module to compute the fault location and fault magnitude. The impact of bi-directional uncertainty and FDD detection time on the overall FTC performance and system recovery is assessed by simulating a quadcopter UAV during a trajectory tracking mission and is found to be robust against incipient actuator faults during straight and level flight and tight turns.</p>
</abstract>

An integrated approach to the fault-tolerant control (FTC) of a quadcopter unmanned aerial vehicle (UAV) with incipient actuator faults is presented. The framework is comprised of a radial basis function neural network (RBFNN) fault detection and diagnosis (FDD) module and a reconfigurable flight controller (RFC) based on the extremum seeking control approach. The dynamics of a quadcopter subject to incipient actuator faults are estimated using a nonlinear identification method comprising a continuous forward algorithm (CFA) and a modified golden section search (GSS) one. A time-difference-of-arrival (TDOA) method and the post-fault system estimates are used within the FDD module to compute the fault location and fault magnitude. The impact of bi-directional uncertainty and FDD detection time on the overall FTC performance and system recovery is assessed by simulating a quadcopter UAV during a trajectory tracking mission and is found to be robust against incipient actuator faults during straight and level flight and tight turns.

Integrated Fault–Tolerant Control of a Quadcopter UAV with Incipient Actuator Faults

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An integrated approach to the fault-tolerant control (FTC) of a quadcopter unmanned aerial vehicle (UAV) with incipient actuator faults is presented. The...

Integrated Fault–Tolerant Control of a Quadcopter UAV with Incipient Actuator Faults

Data publikacji: 30 gru 2022

Zakres stron: 601 - 617

Otrzymano: 23 paź 2021

Przyjęty: 04 maj 2022

DOI: https://doi.org/10.34768/amcs-2022-0042

Słowa kluczowe
fault-tolerant control, quadrocopter, incipient actuator fault, radial basis function neural network

© 2022 Paulin Kantue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Integrated Fault–Tolerant Control of a Quadcopter UAV with Incipient Actuator Faults

Data publikacji: 30 gru 2022

Zakres stron: 601 - 617

Otrzymano: 23 paź 2021

Przyjęty: 04 maj 2022

DOI: https://doi.org/10.34768/amcs-2022-0042

Słowa kluczowefault-tolerant control, quadrocopter, incipient actuator fault, radial basis function neural network

© 2022 Paulin Kantue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
fault-tolerant control, quadrocopter, incipient actuator fault, radial basis function neural network