Active Fault Diagnosis and Major Actuator Failure Accommodation: Application to a UAV

Bateman, François; Noura, Hassan; Ouladsine, Mustapha

doi:10.5772/15207

Cited by 8 publications

(4 citation statements)

References 11 publications

(12 reference statements)

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“…In the literature [22], Elgarsma et al proposed a method to identify the plant parameters by using the test signal without affecting the attitude of the aircraft, based on the input redundancy. This idea has been applied to the AFD technique, and there are several related studies of this application in the field of aircraft control [25][26][27][28]. Bateman et al proposed an AFD method in which the test signal is inputted into the null-space of the input coefficient matrix [25].…”

Section: Introductionmentioning

confidence: 99%

Active fault-diagnosis method using adaptive allocator and fault-tolerant adaptive control system design

et al. 2020

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A new approach to the active fault diagnosis (AFD) for input redundant plants is presented in this paper. A test signal which helps the diagnosis is injected to the plant in addition to nominal control input in the AFD typically. One feature of the proposed AFD is adaptive allocation of the test signal. The adaptive allocator which distributes the test signal and injects it to the redundant actuators is introduced in this paper. A fault-diagnosis (FD) system that estimates fault location and its magnitude from adaptive parameter of the adaptive allocator is constructed with a simple neural network (NN) model. Furthermore, a A fault-tolerant adaptive control system which includes the adaptive test signal allocator is designed based on the model reference adaptive control (MRAC) technique. The adaptive laws of the adaptive allocator and controller are derived by using a suitable Lyapunov function. By performing experiments using a two-input redundant plant, we show the effectiveness and applicability of the proposed AFD and MRAC system.

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Section: Introductionmentioning

confidence: 99%

Active fault-diagnosis method using adaptive allocator and fault-tolerant adaptive control system design

et al. 2020

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“…13,14 The basic idea underlying this work is that major faults in position-controlled flight controls can be detected by a position-tracking monitor (PTM), provided that an accurate predictor is used. 15,16 This would simplify the software complexity as well as the number of additional sensors needed for the health monitoring functions. In this article, starting from the architecture of a fault-tolerant electromechanical actuator for a medium-altitude long-endurance (MALE) UAS ( Figure 1), two model-based positiontracking algorithms are developed and compared in terms of performance.…”

Section: Introductionmentioning

confidence: 99%

Health monitoring of electromechanical flight actuators via position-tracking predictive models

Rito

Schettini

2018

Advances in Mechanical Engineering

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This article deals with the development and performance characterisation of model-based health monitoring algorithms for the detection of faults in an electromechanical actuator for unmanned aerial system flight controls. Two real-time executable position-tracking algorithms, based on predictors with different levels of complexity, are developed and compared in terms of false alarm rejection and fault detection capabilities, using a high-fidelity model of the actuator in which different types of faults are injected. The algorithms' performances are evaluated by simulating flight manoeuvres with the actuator in normal operation as well as with relevant faults (motor coil faults, motor magnet degradation, voltage supply decrease). The results demonstrate that an accurate position-tracking monitor allows to obtain a prompt fault detection and fail-safe mode engagement, while more detailed monitoring functions can be used for fault isolation only.

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“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2049-6427.htm manned aircraft have been transferred to UAVs to ensure that the occurrence of ACs is detected timely and that their effects are mitigated. A partial list of such methods would include the use of banks of observers (Bateman et al, 2011), residuals between model predictions and parameter estimation (Hansen, 2012), Kalman filtering (Khan, 2016), multiple system models (Fekri et al, 2009), dynamic thresholds (Ortiz and Neogi, 2008) and artificial neural networks (Perhinschi et al, 2007). All these efforts have been focused on simplified and isolated scenarios and the need to overcome their lack of generality and develop holistic solutions has been acknowledged (Belcastro and Jacobson, 2010).…”

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confidence: 99%

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

McLaughlin

Perhinschi

2020

IJIUS

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PurposeAn artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.Design/methodology/approachAn AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.FindingsThe proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.Research limitations/implicationsThe promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.Practical implicationsThe use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.Originality/valueIn this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

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Active Fault Diagnosis and Major Actuator Failure Accommodation: Application to a UAV

Cited by 8 publications

References 11 publications

Active fault-diagnosis method using adaptive allocator and fault-tolerant adaptive control system design

Active fault-diagnosis method using adaptive allocator and fault-tolerant adaptive control system design

Health monitoring of electromechanical flight actuators via position-tracking predictive models

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

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