UAV fault-tolerant control by combined L&lt;inf&gt;1&lt;/inf&gt; adaptive backstepping and fault-dependent control allocation

Sørensen, Mikkel Eske Nørgaard; Breivik, Morten

doi:10.1109/cca.2015.7320884

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…A control allocation scheme combined with an L1 adaptive backstepping controller was proposed in [86], as a strategy to achieve fault tolerance in an aircraft's nonlinear longitudinal motion control. Simulations were performed on a Cessna 182 platform and showed remarkable outcomes for nominal as well as defective cases.…”

Section: Fault Tolerant Control Methods In Uavsmentioning

confidence: 99%

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

Fourlas

Karras

2021

Machines

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The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle.

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Section: Fault Tolerant Control Methods In Uavsmentioning

confidence: 99%

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

Fourlas

Karras

2021

Machines

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“…16,17 When an aircraft fails, its aerodynamic model is changed to produce matched or unmatched uncertainties, and then L1 adaptive method is applied to deal with the uncertainties and compensate for the effects of failures. [18][19][20] Meanwhile, in order to enhance the controller's performance, the conventional architecture of L1 adaptive control has also been improved in four aspects, including filter, state predictor, adaptation laws of parameters and control law. [21][22][23][24][25] Dynamic inversion (DI) is a frequently used nonlinear control method.…”

Section: Introductionmentioning

confidence: 99%

L1 adaptive dynamic inversion attitude control for unmanned aerial vehicle with actuator failures

Yan

Liu

Guo

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents a hybrid attitude control scheme of L1 adaptive and dynamic inversion for unmanned aerial vehicle with actuator faults, where both gain fault and bias fault are considered. Firstly, dynamic inversion is employed to track attitude angles in the outer loop and ensures the rapid response. Secondly, an L1 adaptive controller for the inner loop is established to compensate for system uncertainty and uncertainty caused by actuator failures. Thirdly, the analysis of steady-state and transient performance is given by Lyapunov theory. Finally, simulation results prove the effectiveness of the proposed approach.

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“…However, many small UAVs are not over-actuated and hence using the CA from [1] is not possible. This paper is based on the work in [23]. Here, we suggest a new control allocation approach to handle the non-over-actuated control surface configuration usually found on smaller aircraft.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of L1-AB and CA is explored in [22] to control an F16 in a fault-free case. In [23], a fault-dependent control allocation scheme was developed and combined with L1-AB. In [1], SMC and CA is combined to analyse the performance for FTC applications.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Controllers with Fault-Dependent Control Allocation for UAVs

Sørensen

Hansen

Breivik

et al. 2017

J Intell Robot Syst

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This paper combines fault-dependent control allocation with three different control schemes to obtain fault tolerance in the longitudinal control of unmanned aerial vehicles. The paper shows that faultdependent control allocation is able to accommodate actuator faults that would otherwise be critical and it makes a performance assessment for the different control algorithms: an L 1 adaptive backstepping controller; a robust sliding mode controller; and a standard PID controller. The actuator faults considered are the partial to total loss of the elevator, which is a critical component for the safe operation of unmanned aerial vehicles. During nominal operation, only the main actuator, namely the elevator, is active for pitch control. In the event of a partial or total loss of the elevator, faultdependent control allocation is used to redistribute control to available healthy actuators. Using simulations of a Cessna 182 aircraft model, controller performance and robustness are evaluated by metrics that assess control accuracy and energy use. System uncertainties are investigated over an envelope of pertinent variation, showing that sliding mode and L 1 adaptive backstepping provide robustness, where PID control falls short. Additionally, a key finding is that the fault-dependent

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UAV fault-tolerant control by combined L<inf>1</inf> adaptive backstepping and fault-dependent control allocation

Cited by 6 publications

References 24 publications

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

L1 adaptive dynamic inversion attitude control for unmanned aerial vehicle with actuator failures

Performance Comparison of Controllers with Fault-Dependent Control Allocation for UAVs

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