Fault-Tolerant Flight Control for Aircraft Wing Icing Based on Icing Detection Method

2018 ◽  
Author(s):  
Di Ding ◽  
Jing Che ◽  
Wei Q. Qian
Keyword(s):  
Flight Control ◽  
Detection Method ◽  
Fault Tolerant ◽  
Aircraft Wing

Robust fault-tolerant flight control of quadrotor against faults in multiple motors

2021 ◽  
pp. 095441002199954
Author(s):  
Dinesh D Dhadekar ◽  
S E Talole
Keyword(s):  
Flight Control ◽  
Tracking Control ◽  
Fault Tolerant ◽  
External Disturbances ◽  
Detection And Identification ◽  
Attitude Tracking ◽  
Fault Detection And Identification ◽  
Attitude Tracking Control ◽  
Disturbance Estimator

In this article, position and attitude tracking control of the quadrotor subject to complex nonlinearities, input couplings, aerodynamic uncertainties, and external disturbances coupled with faults in multiple motors is investigated. A robustified nonlinear dynamic inversion (NDI)-based fault-tolerant control (FTC) scheme is proposed for the purpose. The proposed scheme is not only robust against aforementioned nonlinearities, disturbances, and uncertainties but also tolerant to unexpected occurrence of faults in multiple motors. The proposed scheme employs uncertainty and disturbance estimator (UDE) technique to robustify the NDI-based controller by providing estimate of the lumped disturbance, thereby enabling rejection of the same. In addition, the UDE also plays the role of fault detection and identification module. The effectiveness and benefits of the proposed design are confirmed through 6-DOF simulations and experimentation on a 3-DOF Hover platform.

EXPERIENCE IN METRICS AND MEASUREMENTS FOR N-VERSION PROGRAMMING

1994 ◽  
Vol 01 (01) ◽  
pp. 41-62 ◽  
Author(s):  
MICHAEL R. LYU ◽  
JIA-HONG CHEN ◽  
ALGIRDAS AVIŽIENIS
Keyword(s):  
Life Cycle ◽  
Flight Control ◽  
Fault Tolerant ◽  
Structural Diversity ◽  
Control Software ◽  
Baseline Design ◽  
Software Diversity ◽  
Points Of View ◽  
Design Diversity ◽  
Diversity Measurement

The N-Version Programming (NVP) approach applies the idea of design diversity to obtain fault-tolerant software units, called N-Version Software (NVS) units. The effectiveness of this approach is examined by the software diversity achieved in the member versions of an NVS unit. We define and formalize the concept of design diversity and software diversity in this paper. Design diversity is a property naturally applicable to the NVP process to increase its fault-tolerance attributes. The baseline design diversity is characterized by the employment of independent programming teams in the NVP. More design diversity investigations could be enforced in the NVP design process, including different languages, different tools, different algorithms, and different methodologies. Software diversity is the resulting dissimilarities appearing in the NVS member versions. We characterize it from four different points of view that are designated as: structural diversity, fault diversity, tough-spot diversity, and failure diversity. Our goals are to find a way to quantify software diversity and to investigate the measurements which can be applied during the life cycle of NVS to gain confidence that operation will be dependable when NVS is actually employed. The versions from a six-language N-Version Programming project for fault-tolerant flight control software were used in the software diversity measurement.

Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 740-746
Author(s):  
Hang Yuan ◽  
Chen Lu ◽  
Ze Tao Xiong ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Simulation Model ◽  
Working Conditions ◽  
Detection Method ◽  
Fault Tolerant ◽  
Adaptive Threshold ◽  
Residual Error ◽  
The Other ◽  
Aerodynamic Loads

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

Dynamic Sensor Allocation Framework for Fault Tolerant Flight Control

2014 ◽  
Vol 47 (3) ◽  
pp. 3477-3482 ◽  
Author(s):  
Tamás Peni ◽  
Bálint Vanek ◽  
Zoltán Szabó ◽  
József Bokor
Keyword(s):  
Flight Control ◽  
Fault Tolerant

scholarly journals Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 236 ◽  
Author(s):  
Nengsong Peng ◽  
Weiwei Zhang ◽  
Hongfei Ling ◽  
Yuzhao Zhang ◽  
Lixin Zheng
Keyword(s):  
Sensor Networks ◽  
Anomaly Detection ◽  
Sensor Network ◽  
Detection Method ◽  
Fault Tolerant ◽  
False Negative ◽  
False Negative Rate ◽  
Temporal Correlation ◽  
Sensor Nodes ◽  
Wireless Sensor

A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%.

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