Modelling and Simulation of a Fault-Tolerant Flight Control System

Perhinschi, Mario G.; Campa, Giampiero; Napolitano, Marcello R.; Lando, Marco; Massotti, Luca; Fravolini, Mario Luca

doi:10.1080/02286203.2006.11442345

Cited by 22 publications

(11 citation statements)

References 4 publications

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“…The aerodynamic and thrust characteristics are provided through 42 look-up tables, with 16 tables for the longitudinal dynamics as functions of Mach number, angle of attack, and stabilator deflection; 20 tables for the lateral dynamics as functions of Mach number, angle of attack, sideslip angle, and rudder deflection; 2 tables for engine thrust and fuel flow as functions of Mach number and altitude. The look-up tables have further been subdivided to isolate the contribution of individual control surfaces in order to be able to simulate control surface failures [21]. The scheme also features a set of pre-trained neural networks (PTNNs) to provide updated values of the aircraft aerodynamic coefficients once every 10 s, as it flies through different regions of its flight envelope.…”

Section: Ftfcs and Fdii Algorithm For Actuator/sensor Failuresmentioning

confidence: 99%

Evaluation of a Fault-Tolerant Scheme in a Six-Degree-of-Freedom Motion Flight Simulator

Sagoo

Gururajan

Seanor

et al. 2010

Journal of Aerospace Computing, Information, and Communication

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Section: Ftfcs and Fdii Algorithm For Actuator/sensor Failuresmentioning

confidence: 99%

Evaluation of a Fault-Tolerant Scheme in a Six-Degree-of-Freedom Motion Flight Simulator

Sagoo

Gururajan

Seanor

et al. 2010

Journal of Aerospace Computing, Information, and Communication

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“…left and right control surface contributions. A failure involving a locked actuator does not affect the aerodynamic characteristics of the control surface; however, under these failure conditions the aircraft mathematical model must include the contribution of each left and right surface 19 , since individual control surface deflections affect both the longitudinal and lateral-directional dynamic responses of the aircraft. For example, following a failure on one of the stabilators, there is a coupling of the longitudinal and lateral directional aircraft dynamics, resulting in roll and yaw responses when there is an input from the healthy stabilator.…”

Section: System Identificationmentioning

confidence: 99%

“…Eqs. (17)(18)(19)(20)(21)(22) represent the total aircraft drag, lift, pitching moment, aerodynamic side-force, rolling moment, and yawing moment coefficients, respectively 27 .…”

Section: B Nominal Nonlinear Aircraft Modelingmentioning

confidence: 99%

“…This inaccuracy was most likely due to the large amount of states that required estimation with the individual doublet inputs. Therefore, a full state matrix (8 states) was built by combining the longitudinal and lateral-directional nominal linear models, and two different methods were used to identify the input matrix components affected by the control surface failures, that is the Normalized Recursive Least Squares (NRLS) 19 method and the Fourier Transform Regression (FTR) 18,29-34 method.…”

Section: Codice Linear Model Identificationmentioning

confidence: 99%

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Nonlinear Aircraft Model Identification and Validation for a Fault-Tolerant Flight Control System

Phillips

Gururajan

Campa

et al. 2010

AIAA Atmospheric Flight Mechanics Conference

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This paper presents the results of a parameter identification study for determining a mathematical model of the WVU YF-22 unmanned research aircraft under both nominal and actuator failure conditions to simulate malfunctions on primary control surfaces. Specifically, both linear and nonlinear mathematical models were derived from measured flight data acquired from pilot and automated computer-injected maneuvers under nominal and failure conditions. From analysis, the stability and control derivatives were extracted to determine the aerodynamic forces and moments. The aerodynamic derivatives were then introduced into a simulation model implemented within a Simulink-based environment, and studies were conducted to validate the accuracy of the identified models. Additionally, "Empirical" and DATCOM analyses were conducted for the WVU YF-22 to further validate the nonlinear model obtained through the parameter identification study. Nomenclature a = linear acceleration (m/s 2 ) A = decoupled (failure) state matrix b = wing span (m) B = decoupled (failure) input matrix C = aerodynamic coefficient c = mean aerodynamic chord (m) e = error H = altitude (m) i = surface deflection (deg) I = moment of inertia (kg m 2 ) J = product of inertia (kg m 2 ) m = aircraft mass (kg) TP 1 2 p = roll rate (deg/s) q = pitch rate (deg/s) q = dynamic pressure (psi) r = yaw rate (deg/s) S = wing surface area (m 2 ) T = thrust (N) V = velocity (m/s) Greek Letters α = angle of attack (deg) β = angle of sideslip (deg) δ = control surface deflection (deg) θ = pitch angle (deg)  = roll angle (deg) ψ = yaw angle (deg) ρ = air density (kg/m 3 ) Subscripts A = aileron D = drag H = stabilator l = rolling moment L = lift, left m = pitching moment n = yawing moment R = rudder, right xx = about the x-axis (body) xz = about the x and z axes (body) Y = side force yy = about the y-axis (body) zz = about the z-axis (body) Downloaded by PURDUE UNIVERSITY on July 30, 2015 | http://arc.aiaa.org |

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“…Finally, the separation of the contributions of the control surfaces (left and right) to the derivatives allows the computation of forces and moments at post-failure conditions as functions of the efficiency parameters. More details on the general modeling of the actuator failures are presented by Perhinschi et al (2006a).…”

Section: Sensor Failure Accommodationmentioning

confidence: 99%

A simulation environment for design and testing of aircraft adaptive fault‐tolerant control systems

Perhinschi

Napolitano

Campa

2008

Aircraft Engineering and Aerospace Technology

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PurposeThe purpose of this paper is to present the development of a Matlab/Simulink‐based simulation environment for the design and testing of indirect and direct adaptive flight control laws with fault tolerant capabilities to deal with the occurrence of actuator and sensor failures.Design/methodology/approachThe simulation environment features a modular architecture and a detailed graphical user interface for simulation scenario set‐up. Indirect adaptive flight control laws are implemented based on an optimal control design and frequency domain‐based online parameter estimation. Direct adaptive flight control laws consist of non‐linear dynamic inversion performed at a reference nominal flight condition augmented with artificial neural networks (NNs) to compensate for inversion errors and abnormal flight conditions following the occurrence of actuator or sensor failures. Failure detection, identification, and accommodation schemes relying on neural estimators are developed and implemented.FindingsThe simulation environment provides a valuable platform for the evaluation and validation of fault‐tolerant flight control laws.Research limitations/implicationsThe modularity of the simulation package allows rapid reconfiguration of control laws, aircraft model, and detection schemes. This flexibility allows the investigation of various design issues such as: the selection of control laws architecture (including the type of the neural augmentation), the tuning of NN parameters, the selection of parameter identification techniques, the effects of anti‐control saturation techniques, the selection and the tuning of the control allocation scheme, as well as the selection and tuning of the failure detection and identification schemes.Originality/valueThe novelty of this research efforts resides in the development and the integration of a comprehensive simulation environment allowing a very detailed validation of a number of control laws for the purpose of verifying the performance of actuator and sensor failure detection, identification, and accommodation schemes.

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Modelling and Simulation of a Fault-Tolerant Flight Control System

Cited by 22 publications

References 4 publications

Evaluation of a Fault-Tolerant Scheme in a Six-Degree-of-Freedom Motion Flight Simulator

Evaluation of a Fault-Tolerant Scheme in a Six-Degree-of-Freedom Motion Flight Simulator

Nonlinear Aircraft Model Identification and Validation for a Fault-Tolerant Flight Control System

A simulation environment for design and testing of aircraft adaptive fault‐tolerant control systems

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