Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

Perhinschi, Mario G.; Moncayo, Hever; Azzawi, Dia Al

doi:10.2514/1.c032381

Cited by 21 publications

(13 citation statements)

References 34 publications

(57 reference statements)

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“…It is important to notice that the general architecture of the identification problem requires prior generation of the non-self with adequate resolution and knowledge of the abnormal condition characteristics. In previous efforts, this problem was addressed by considering the abnormal condition identification and evaluation as independent modules (16,17) . First, identification was defined as the determination of the subsystem affected by the abnormal condition.…”

Section: Ais-based Fdie Problem Formulationmentioning

confidence: 99%

“…The artificial immune system (AIS) paradigm has shown promising potential in a variety of applications such as pattern recognition (3,4) , robotics (5,6) , computer security (7,8) , data mining (9,10) , adaptive controls (11)(12)(13) and anomaly detection (14,15) . Researchers from West Virginia University (WVU) and Embry-Riddle Aeronautical University (ERAU) have proposed an integrated framework for AIS-based solutions to the aircraft sub-system failure detection, identification, evaluation (FDIE) problem (16,17) . As part of this effort, a comprehensive set of methodologies for AIS design and optimisation have been developed and implemented (18)(19)(20) .…”

Section: Introductionmentioning

confidence: 99%

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Structured non-self approach for aircraft failure identification within a fault tolerance architecture

et al. 2016

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Within an immunity-based architecture for aircraft fault detection, identification and evaluation, a structured, non-self approach has been designed and implemented to classify and quantify the type and severity of different aircraft actuators, sensors, structural components and engine failures. The methodology relies on a hierarchical multi-self strategy with heuristic selection of sub-selves and formulation of a mapping logic algorithm, in which specific detectors of specific selves are mapped against failures based on their capability to selectively capture the dynamic fingerprint of abnormal conditions in all their aspects. Immune negative and positive selection mechanisms have been used within the process. Data from a motion-based

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Section: Ais-based Fdie Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structured non-self approach for aircraft failure identification within a fault tolerance architecture

et al. 2016

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“…A variety of control system design methodologies have been developed, starting with simple stability augmentation systems and ending up with sophisticated adaptive control laws [5][6][7]. Significant recent research efforts were aimed at increasing aircraft operation safety through the development of integrated fault tolerant control systems capable of high performance real time failure detection, identification, evaluation, and accommodation (FDIEA) [8][9][10].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Immunity-based accommodation of aircraft subsystem failures

Togayev¹

2017

AEAT

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Immunity-Based Accommodation of Aircraft Subsystem Failures This thesis presents the design, development, and flight-simulation testing of an artificial immune system (AIS) based approach for accommodation of different aircraft subsystem failures. Failure accommodation is considered as part of a complex integrated AIS scheme that contains four major components: failure detection, identification, evaluation, and accommodation. The accommodation part consists of providing compensatory commands to the aircraft under specific abnormal conditions based on previous experience. In this research effort, the possibility of building an AIS allowing the extraction of pilot commands is investigated. The proposed approach is based on structuring the self (nominal conditions) and the non-self (abnormal conditions) within the AIS paradigm, as sets of artificial memory cells (mimicking behavior of T-cells, B-cells, and antibodies) consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight including pilot inputs, system states, and other variables. The accommodation algorithm relies on identifying the memory cell that is the most similar to the incoming measurements. Once the best match is found, control commands corresponding to this match will be extracted from the memory and used for control purposes. The proposed methodology is illustrated through simulation of simple maneuvers at nominal flight conditions, different actuators, and sensor failure conditions. Data for development and demonstration have been collected from West Virginia University 6-degreesof-freedom motion-based flight simulator. The aircraft model used for this research represents a supersonic fighter which includes model following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The simulation results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and the capability of the AIS paradigm to address the problem of accommodating actuator and sensor malfunctions as a part of a comprehensive and integrated framework along with abnormal condition detection, identification, and evaluation. DEDICATION To my mother Gulnara Yezhebayeva and my grandparents,

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“…However, there are not so many available significant research efforts directly addressing these abnormal conditions and failures on UAVs. Artificial immune system (AIS) [22] is one recent technique that has been used to combat abnormal conditions on UAVs. It works comparable to the biological immune system in accordance with the self-nonself discrimination principle.…”

Section: General Abnormal Conditionsmentioning

confidence: 99%

UAV Modeling and Simulation at Normal and Abnormal Conditions

Fagbemi¹

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The main objective of this thesis is to develop new capabilities within the West Virginia University (WVU) unmanned aerial systems (UAS) simulation environment for the design and analysis of fault tolerant control laws on small sized unmanned aerial vehicles (UAVs). An aerodynamic model for an electric powered UAV is developed using a vortex lattice method implemented within the computational design package Tornado. One-dimensional look-up tables are developed for the main stability and control derivatives, which are then used to calculate linear aerodynamic forces and moments for the nonlinear aircraft equations of motion. Flight data are used for model verification and tuning. The characteristics under normal and abnormal operation of various types of sensors typically used for UAV control are classified under nine functional categories. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each functional category. Simple mathematical and logical algorithms are formulated and used in this process. Each functional category is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. The aircraft and sensor models are then integrated with the WVU UAS simulation environment, which is created using MATLAB/Simulink for the computational part and FlightGear for the visualization of the aircraft and scenery. A simple user-friendly graphical interface is designed to allow for detailed simulation scenario setup. The functionality of the developed models is illustrated through a limited analysis of the effects of sensor abnormal operation on the trajectory tracking performance of autonomous UAV. A composite metric is used for aircraft performance assessment based on both trajectory tracking errors and control activity. The targeted sensors are the gyroscopes providing angular rate measurements and the global positioning system providing position and velocity information. These sensors are instrumental in the inner and outer control loops, respectively, which characterize the typical control architecture for autonomous trajectory tracking. Due to its generality and flexibility, the proposed sensor model provides detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It facilitates the investigation of the synergistic interactions between sensors and control systems and may lead to improvements in both areas.

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Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

Cited by 21 publications

References 34 publications

Structured non-self approach for aircraft failure identification within a fault tolerance architecture

Structured non-self approach for aircraft failure identification within a fault tolerance architecture

Immunity-based accommodation of aircraft subsystem failures

UAV Modeling and Simulation at Normal and Abnormal Conditions

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