Actuator Fault Detection and Fault-Tolerant Control for Hexacopter

Nguyen, Ngoc Phi; Xuan-Mung, Nguyen; Hong, Sung Kyung

doi:10.3390/s19214721

Cited by 40 publications

(27 citation statements)

References 23 publications

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“…Engine malfunction or failures are among the common faults in multi-rotor drones, which apparently endanger the drone and the people's safety on the ground. In order to increase flight safety and reliability of drones, researchers are working on automation enhancement to safely recover the impaired drone (Lopez-Franco et al 2017;Mazeh et al 2018;Nguyen et al 2019).…”

Section: The Landing System Frameworkmentioning

confidence: 99%

Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations

Turan

Avşar

Asadi

et al. 2021

Turkish Journal of Engineering

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Flight safety and reliability improvement is an important research issue in aerial applications. Multi-rotor drones are vulnerable to motor failures leading to potentially unsafe operations or collisions. Therefore, researchers are working on autonomous landing systems to safely recover and land the faulty drone in on a desired landing area. In such a case, a suitable landing zone should be detected rapidly in for emergency landing. Majority of the works related with autonomous landing utilize a marker and GPS signals to detect landing site. In this work, we propose a landing system framework that involves only the processing of images taken from the onboard camera of the vehicle. First, the objects in the image are determined by filtering and edge detection algorithm, then the most suitable landing zone is searched. The area that is free from obstacles and closest to the center of the image is defined as the most immediate and suitable landing zone. The method has been tested on 25 images taken from different heights and its performance has been evaluated in terms runtime on a single board computer and detection precision and recall values. The average measured runtime is 2.4923 seconds and 100% of precision and recall values are achieved for the images taken from 1m and 2m. The smallest precision and recall values are 79.1% and 81.2%, respectively.

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Section: The Landing System Frameworkmentioning

confidence: 99%

Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations

Turan

Avşar

Asadi

et al. 2021

Turkish Journal of Engineering

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“…In contrast, a few studies have addressed the problem of fault-tolerant control for actuators faults in redundant UAVs providing experimental validations for the proposed strategies, such as in [30], where the authors proposed a fault detection and a fault-tolerant control FTC scheme which can handle up to two actuators failure for an hexarotor. This FTC scheme uses a nonlinear Thau observer to estimate the states of the UAV and to detect the actuators failures.…”

Section: State Of the Art On Fault-tolerant Control For Uavs Through mentioning

confidence: 99%

“…Proposition 1. Given the nonlinear subsystems (17), (18), (19) and 20, by applying the control law (30) and by choosing K i ≥ η i > 0, where η i is any fixed positive value, the condition s i (X) = 0 is satisfied and thus the system errors e i andė i converge to zeros, meaning that the system's state will eventually be the desired one.…”

Section: Self-tuning Sliding Mode Control (Stsmc)mentioning

confidence: 99%

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Comparative study of self tuning, adaptive and multiplexing FTC strategies for successive failures in an Octorotor UAV

Hamadi

Lussier

Fantoni

et al. 2020

Robotics and Autonomous Systems

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This paper presents three fault-tolerant control (FTC) strategies for a coaxial octorotor unmanned aerial vehicle (UAV) regarding motor failures. The first FTC is based on a control mixing strategy which consists of a set of control laws designed offline, each one dedicated to a specific fault situation. The second FTC, a robust adaptive sliding mode control allocation is presented, where the control gains of the controller are adjusted online in order to redistribute the control signals among the healthy motors in order to stabilize the overall system. The third FTC strategy is a new strategy proposed in this article, which is based on a self-tuning sliding mode control (STSMC) where the control gains are readjusted based on the detected error to maintain the stability of the system. Multiple indoor experiments on an octorotor UAV are conducted to show and compare the effectiveness and the behavior of each FTC scheme after successive faults are injected. More specifically, we inject complete actuator's failures into the top four motors of our octorotor. Every strategies show good fault tolerance results, although the control mixing method performs slightly better overall while the adaptive method performs slightly worse. However, the control mixing method requires a huge design effort to take into account as much situations as possible, while the adaptive method and the STSMC only require to determine a few gains. The adaptive method do not need fault detection to operate, but it thus does not provide information on the system's health without an additional fault identification and diagnosis mechanism, while both the control mixing method and the STSMC provide such information.

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“…However, this method requires an expensive computation cost due to implementing an online neural network algorithm to estimate unknown parameters. To simplify this complicated design, the idea of disturbance observer-based adaptive SMC (DO-ASMC) algorithm for the attitude and altitude control of hexacopter was proposed in [23]. Based on the observer information, this method is used to detect the normal and faulty cases for the flight controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

et al. 2021

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In this paper, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural network algorithm to achieve the time-varying sliding surface; their coefficients in sliding surface are adjusted through backpropagation law. The disturbance observer is also combined with sliding mode controllers to estimate and handle the external disturbances. Finally, the Lyapunov theory is applied to validate the stability of suggested control method. The performance of proposed sliding mode control has been evaluated using a numerical simulation. The results show that the attitude and altitude controller based on suggested algorithm has a better tracking performance and disturbance rejection.

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Actuator Fault Detection and Fault-Tolerant Control for Hexacopter

Cited by 40 publications

References 23 publications

Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations

Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations

Comparative study of self tuning, adaptive and multiplexing FTC strategies for successive failures in an Octorotor UAV

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

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