An Adaptive Fault Management (AFM) System for Resilient Flight Control

Boÿskovic, Jovan D.; Redding, Joshua; Knoebel, Nathan

doi:10.2514/6.2009-6263

Cited by 16 publications

(10 citation statements)

References 14 publications

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“…The existence of a Failure Detection and Identification (FDI) scheme can support automatic accommodation as part of a fault-tolerant control system and it can also improve human accommodation through increased pilot situational awareness. Most of the research efforts in this area have considered only individual failures within limited regions of the flight envelope [4][5][6]. State estimation or observer-based schemes have been widely proposed [7][8][9][10] for actuator FDI relying on Kalman or other classes of filters.…”

Section: Introductionmentioning

confidence: 99%

Comparison of immunity-based schemes for aircraft failure detection and identification

Azzawi

Moncayo

Perhinschi

et al. 2016

Engineering Applications of Artificial Intelligence

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In this paper, two approaches are proposed and compared for the detection and identification of aircraft subsystem failures based on the artificial immune system paradigm combined with the hierarchical multiself strategy. The first approach relies on the heuristic ranking of lower order self/non-self projections and the generation of selective immunity identifiers through structuring of the non-self. The second approach is based on an information processing algorithm inspired by the functionality of the dendritic cells. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets information from the multiple selves to produce a unique detection and identification outcome. A hierarchical multi-self strategy is used with both approaches considering 2-dimensional self/non-self projections or subselves. A mathematical formulation of the concepts and detailed implementation algorithms are presented. The proposed methodologies are demonstrated and compared using simulation data for a supersonic fighter from a motion-based flight simulator at nominal conditions, under failures of actuators, malfunction of sensors, and wing damage. In all cases considered, both detection and identification schemes achieve excellent detection and identification rates with practically no false alarms.

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Section: Introductionmentioning

confidence: 99%

Comparison of immunity-based schemes for aircraft failure detection and identification

Azzawi

Moncayo

Perhinschi

et al. 2016

Engineering Applications of Artificial Intelligence

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“…The most straight-forward of course is through wind tunnel testing, flight testing, and high-fidelity model-based computation. [5][6][7][8] A more sophisticated analysis is obtained by formulating the envelope estimation problem as a reachability problem, 9 and then solving the associated Hamilton-Jacobi equations, often through the use of level set methods. 10 Further extensions on this theme include leveraging the concepts of timescale separation 11 or semi-Lagrangian level sets.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

Schuet

Lombaerts

Acosta

et al. 2014

AIAA Guidance, Navigation, and Control Conference

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A nonlinear aircraft model is presented and used to develop an overall unified approach to online trim and maneuverability envelope estimation with uncertainty quantification without any requirement for active input excitation. The concept of time scale separation makes this method suitable for the adaptive characterization of altered safe maneuvering limitations based on aircraft performance after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios. Nomenclature R nSet of real n-vectors S n , S n ++ Set of n × n symmetric matrices, Set of n × n positive definite symmetric matrices tr Trace operator, i.e., tr A is the sum of the diagonal elements ofEuclidean norm for vector inputs V, γ, χ True airspeed, flight path angle, course angle F T Magnitude of net thrust force produced by engines F L , F D , F Y Magnitude of lift, drag, and side forces f Continuous dynamics model x, x State vector, Collection of state observations u, u Virtual input vector, Collection of virtual input observations B Set of allowable or achievable states and input vectors x Average current and next state, i.e.,x(Angle of attack, Side-slip angle, Roll angle g, m, S Acceleration due to gravity, Mass of the aircraft, Surface area of the aircraft wings ρ,qAir density, Dynamic pressure (q = ρV 2 /2) N (μ, Σ)Multivariate normal distribution with mean μ and covariance matrix Σ. p(x|y)Probability density function for the variable x given y L i , D j , Y k Expansion coefficients for the non-dimensional coefficient of lift, drag, and side force c Aerodynamic coefficient parameter vector, e.

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“…Most of the research efforts in the area of abnormal condition detection and identification have focused on individual/isolated classes of failures occurring within limited regions of the flight envelope [5][6][7][8]. Several research efforts have been conducted in the past few years on the design of fault-tolerant adaptive control laws [5,9,10] and the development of envelope estimation and protection methodologies for aircraft under damage/failure conditions [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

Perhinschi

Moncayo

Azzawi

2014

Journal of Aircraft

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This paper presents the development of a biologically inspired generalized conceptual framework for the detection, identification, evaluation, and accommodation of aircraft subsystem abnormal conditions. The artificial immune system paradigm in conjunction with other artificial intelligence techniques, analytical tools, and heuristics are used in an attempt to provide a comprehensive solution to the problem of safely operating aircraft under abnormal flight conditions. The main concepts and foundations are established, and methodologies and algorithms for implementation are outlined. The approach addresses directly the complexity and multidimensionality of aircraft dynamic response in the context of abnormal conditions and is expected to facilitate the design of onboard augmentation systems to increase aircraft survivability, improve operation safety, and optimize performance at both normal and abnormal/upset conditions. Results obtained with an example implementation are presented to illustrate the potential of the proposed framework for producing high-performance schemes for aircraft subsystem abnormal condition detection, identification, evaluation, and accommodation.

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An Adaptive Fault Management (AFM) System for Resilient Flight Control

Cited by 16 publications

References 14 publications

Comparison of immunity-based schemes for aircraft failure detection and identification

Comparison of immunity-based schemes for aircraft failure detection and identification

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

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