Actuator fault detection system for a mini-quadrotor

Freddi, Alessandro; Longhi, Sauro; Monteriù, Andrea

doi:10.1109/isie.2010.5637750

Cited by 59 publications

(41 citation statements)

References 14 publications

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“…The attitude PD controller is embedded inside the Asctec Autopilot. The dynamic model of the drone used to develop the control system is based on the work of Freddi [18] based on the Lagrangian approach. The six second order equations of the dynamics are the following:…”

Section: Control Systemmentioning

confidence: 99%

“…When this happens it performs a structured reduction of rotor thrust which is independent from altitude controller. Knowing the rate of decrease of the thrust, and the current drone height and thrust, it is possible to calculate from the third equation in (18) the required time to touch the ground. Knowing the speed of the drone provided by the vision algorithm, its current position and the time to reach ground, the system checks if it falls (calculating the landing error at ground) inside the centering system even in presence of position oscillation due to stabilization.…”

Section: Control Systemmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous navigation, landing and recharge of a quadrotor using artificial vision

Cocchioni

Mancini

Longhi

2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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In this paper a solution to UAV reduced endurance and autonomous flight is proposed. With a complete on-board solution, based on artificial vision, the developed system is able to autonomously take off, navigate and land, recharging its battery by using a dedicated landing platform, both in indoor and outdoor scenarios. The landing platform includes a passive centering system to correct the landing error of the UAV, with a novel design wich reduce cost and increase the safety (thanks to small and isolated electrical contacts) without invasive hardware changes on the drone. The developed vision algorithm provides a fast and accurate measurement of UAV position with respect to the landing platform using a visual target, but at the same time it is able to automatically switch to an estimation of position that is independent from the visual target. This aspect is used during navigation or when the tracking of the target fails, ensuring a continuous position measurement feed to the controllers. The developed control system manages all the different phases of a mission (motor turning on/off, take off, navigation, landing, . . . ) with low control error, ensuring a landing over the landing platform with an error that is lower than 5cm for both x and y axis. The developed software in ROS environment is modular and provides input/output interfaces to receive command, or send data.

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Section: Control Systemmentioning

confidence: 99%

Section: Control Systemmentioning

confidence: 99%

Autonomous navigation, landing and recharge of a quadrotor using artificial vision

Cocchioni

Mancini

Longhi

2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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show abstract

“…They utilize residuals (difference between the sensor readings and expected values derived from a model of the monitored system) for the detection of occurrence of a fault. An example of an actuator fault diagnostic system designed for the nonlinear model of mini-quadrotors is available in [17]. Deployment of a methodology for actuator and sensor fault detection in an autonomous helicopter is presented in [18].…”

Section: State Of the Artmentioning

confidence: 99%

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Saska

Krajník

Vonásek

et al. 2013

J Intell Robot Syst

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“…The model-based approach considers a mathematical model of the system to monitor the behavior of the plant and generate residuals to diagnose the fault in real-time [2][3][4][5][6]. The complexity of extracting the exact model, which imitates the system dynamics, is one of the worrying and challenging tasks of using the model-based approach [7,8]. In some cases of sensor failures, the modelbased approach will fail to provide the correct data to the active FTC unit, which could lead to a catastrophic failure for the whole system.…”

Section: Introductionmentioning

confidence: 99%

Sensor fault diagnostic and Fault-Tolerant Control for the altitude control of a quadrotor UAV

Drak

Noura

Hejase

et al. 2015

2015 IEEE 8th GCC Conference &Amp; Exhibition

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The two major occurring faults with relevance to UAVs are actuator and sensor faults. This paper presents a fault diagnostic and fault tolerant control algorithm on the altitude sensor of a quadrotor UAV. The quadrotor used is QUANSER's Qball-X4, which is part of the Unmanned Vehicles Lab (UVL) at UAE University. The proposed fault tolerant control is based on hardware redundancy of three altitude sensors. Residuals are generated based on the three altitude measurements, which are then used to compute indicators about fault occurrences. These indicators serve the fault isolation algorithm to determine the faulty sensor, and in return allows for automatic switching between the sensors in case the faulty sensor is used in the closedloop system. The effectiveness of the proposed algorithm is demonstrated by means of real flight tests.

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Actuator fault detection system for a mini-quadrotor

Cited by 59 publications

References 14 publications

Autonomous navigation, landing and recharge of a quadrotor using artificial vision

Autonomous navigation, landing and recharge of a quadrotor using artificial vision

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Sensor fault diagnostic and Fault-Tolerant Control for the altitude control of a quadrotor UAV

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