X-43A Flush Airdata Sensing System Flight-Test Results

Baumann, Ethan; Pahle, Joseph W.; Davis, Mark C.; White, John T.

doi:10.2514/1.41163

Cited by 52 publications

(19 citation statements)

References 10 publications

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“…Remark 5: As reported in [48], hydraulic driven actuators are configured in hypersonic vehicles to operate all control surfaces. Flush air data system (FADS) that is often mounted in the upper and lower lifting surfaces has been successfully applied to hypersonic vehicles [49]. FADS, which is dependent on the pressure sensor array measurement of aircraft surface pressure distribution, obtains dynamic pressure, bank angle, AOA, and sideslip angle indirectly through a specific algorithm.…”

Section: B Control-oriented Model Subject To Actuator Faultsmentioning

confidence: 99%

The Design of Fixed-Time Observer and Finite-Time Fault-Tolerant Control for Hypersonic Gliding Vehicles

Li¹,

2018

IEEE Trans. Ind. Electron.

244

104

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This paper proposes a fault-tolerant control scheme for a hypersonic gliding vehicle to counteract actuator faults and model uncertainties. Starting from the kinematic and aerodynamic models of the hypersonic vehicle, the control-oriented model subject to actuator faults is built. The observers are designed to estimate the information of actuator faults and model uncertainties, and to guarantee the estimation errors for converging to zero in fixed settling time. Subsequently, the finite-time multivariable terminal sliding mode control and composite-loop design are pursued to enable integration into the faulttolerant control, which can ensure the safety of the postfault vehicle in a timely manner. Simulation studies of a six degree-of-freedom nonlinear model of the hypersonic gliding vehicle are carried out to manifest the effectiveness of the investigated fault-tolerant control system. Index Terms-Hypersonic gliding vehicle, actuator faults and model uncertainties, fixed-time observer, finite-time fault-tolerant control.

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Section: B Control-oriented Model Subject To Actuator Faultsmentioning

confidence: 99%

The Design of Fixed-Time Observer and Finite-Time Fault-Tolerant Control for Hypersonic Gliding Vehicles

Li¹,

2018

IEEE Trans. Ind. Electron.

244

104

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“…In particular, time-varying shocks and flame propagation in the combustor can result in very large, rapid variations in temperature and heat flux. [3][4][5][6][7] Commercial aftermarket sensors often provide limited utility for such environments due to their size, profile, weight, temperature limitations, and integration complexity. The development of sensors and instrumentation capabilities specifically for hypersonic vehicles would substantially accelerate their advancement for use in global strike and space access programs.…”

Section: Introductionmentioning

confidence: 99%

Heat Flux Measurements in a Scramjet Combustor Using Direct Write Technology

Kennedy¹,

Donbar²,

Afb³

et al. 2011

17th AIAA International Space Planes and Hypersonic Systems and Technologies Conference

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In this experimental study, heat flux measurements were made at four locations within the isolator/combustor/nozzle region of a direct-connect gaseous hydrocarbon-fueled scramjet combustor. Combustor conditions were fixed and representative of a Mach-5 flight condition. The heat flux measurements were made using sensors deposited directly on the engine thermal barrier coating (TBC) using Direct Write Technology. Flush-wall fuel injection locations were fixed and a cavity on the body side wall provided the necessary flame holder. Overall equivalence ratios were varied from 0.6 to 1.0, but were held constant for a given run. Preliminary results show heat flux values rising with combustor equivalence ratio at all measurement locations. Heat flux values ranging from 400 to 1000 kW/m 2 (35 to 88 BTU/ft 2 -s) were measured in the location downstream of the cavity flame holder. Heat flux values ranging from 600 to 2000 kW/m 2 (53 to 176 BTU/ft 2 -s) were measured in the cavity flame holder location. Heat flux values ranging from 200 to 600 kW/m 2 (18 to 53 BTU/ft 2 -s) were measured in the locations upstream of the cavity flame holder. Measured local heat flux values were consistent with area-averaged heat flux measurements over a section of the combustor. Nomenclature D = depth of cavity k = thermal conductivity L = length of cavity N = number of thermopile elements k = thermal conductivity P = pressure S = Seebeck effect T = temperature   fuel equivalence ratio Q = flight dynamic pressure L x = thickness of material x R x = thermal resistance of material x q = heat transfer rate = heat flux (heat transfer rate per unit area) = mass flow rate c p = specific heat

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“…One common technique is the use of complimentary filters 3,4 in which the attitude rates from INS are used to smooth the FADS attitude estimate. The complimentary filtering approach does not directly impact any estimates of the atmospheric conditions along the trajectory.…”

Section: Introductionmentioning

confidence: 99%

Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation

Karlgaard

Kutty

Schoenenberger

2015

AIAA Atmospheric Flight Mechanics Conference

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This paper describes an algorithm for atmospheric state estimation that is based on a coupling between inertial navigation and flush air data sensing pressure measurements. In this approach, the full navigation state is used in the atmospheric estimation algorithm along with the pressure measurements and a model of the surface pressure distribution to directly estimate atmospheric winds and density using a nonlinear weighted least-squares algorithm. The approach uses a highfidelity model of atmosphere stored in table-look-up form, along with simplified models of that are propagated along the trajectory within the algorithm to provide prior estimates and covariances to aid the air data state solution. Thus, the method is essentially a reduced-order Kalman filter in which the inertial states are taken from the navigation solution and atmospheric states are estimated in the filter. The algorithm is applied to data from the Mars Science Laboratory entry, descent, and landing from August 2012. Reasonable estimates of the atmosphere and winds are produced by the algorithm. The observability of winds along the trajectory are examined using an index based on the discrete-time observability Gramian and the pressure measurement sensitivity matrix. The results indicate that bank reversals are responsible for adding information content to the system. The algorithm is then applied to the design of the pressure measurement system for the Mars 2020 mission. The pressure port layout is optimized to maximize the observability of atmospheric states along the trajectory. Linear covariance analysis is performed to assess estimator performance for a given pressure measurement uncertainty. The results indicate that the new tightly-coupled estimator can produce enhanced estimates of atmospheric states when compared with existing algorithms. Nomenclature C = Backward smoothing gain F = Linearization of f with respect to x f = Low-fidelity atmospheric model equations of motion G = Linearization of f with respect to u g = Gravitational acceleration, m/s 2 H = Linearization of h with respect to x h = Pressure distribution model, Pa I = Identity matrix J = Linearization of h with respect to u k = Integer time index N = Integer time index of final pressure measurement P = Covariance of x after the measurement model update = Static pressure, Pa p = Pressure measurement vector, Pa Q = Process noise spectral densitỹ Q = Process noise covariance R = Pressure measurement covariance matrix R = Pressure measurement covariance matrix augmented with navigation uncertainty R = Planetary radius, m = Specific gas constant, J/kg-K S = Prior covariance of x from low fidelity model T = Prior covariance of x from high fidelity model T = Atmospheric temperature, K u = Vehicle inertial state v n , v e , v d = Vehicle planet-relative north, east, and down velocity components, m/s W o = Discrete-time observability Gramian w n , w e , w d = North, east, and down wind velocity components, m/s X 11 , X 12 , X 22 = Van Loan integral sub-matrices x = Atmospheric sta...

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X-43A Flush Airdata Sensing System Flight-Test Results

Cited by 52 publications

References 10 publications

The Design of Fixed-Time Observer and Finite-Time Fault-Tolerant Control for Hypersonic Gliding Vehicles

The Design of Fixed-Time Observer and Finite-Time Fault-Tolerant Control for Hypersonic Gliding Vehicles

Heat Flux Measurements in a Scramjet Combustor Using Direct Write Technology

Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation

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