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Gust load alleviation for a long-range aircraft with and without anticipation

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Abstract

This paper presents an overview of the DLR activities on active load alleviation in the CleanSky Smart Fixed Wing Aircraft project. The investigations followed two main research directions: the multi-objective, multi-model, structured controller design for the feedback load alleviation part and the use of Doppler LIDAR technologies for gust/turbulence anticipation. On this latter topic, the prior work made in the AWIATOR European FP6 project constituted a reference in terms of demonstrations and the objective was not to repeat these previous investigations with a real sensor in flight test but to develop new ideas for the exploitation of the Doppler LIDAR measurements for gust alleviation purposes. Very fruitful exchanges between industry partners and research organizations took place during this project and all the work presented in this paper has been made using a generic long-range benchmark provided by Airbus on the basis of the XRF-1 model.

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Abbreviations

ALC:

Active load control(ler)

ALDCS:

Active lift distribution control system, active load alleviation system developed for the Lockheed C5-A

AWIATOR:

Aircraft wing advanced technology operation, European FP6 project investigating many innovative technologies for future and more efficient aircraft

BFGS:

Broyden–Fletcher–Goldfarb–Shanno, a well-known quasi-Newton optimization algorithm

\(\hbox {C}^*, \hbox {C}^*\hbox {U}\) :

Control concepts in the pitch axis based on the blending of the load factor and the pitch rate (with airspeed feedback for C\(^*\)U)

DELICAT:

DEmonstration of LIdar-based CAT detection, European FP7 project on the detection of clear air turbulence

DLC:

Direct lift control, control surfaces/effectors permitting to directly control the aircraft lift (i.e., not through variations of the angle of attack)

DLR:

Deutsches Zentrum für Luft- und Raumfahrt (German Aerospace Center)

EFCS:

Electronic flight control system

FBALC:

Feedback active load controller, name of the feedback part of the herein proposed load alleviation functions

FOWT:

Fast orthogonal wavelet transform

FP6:

Sixth framework programme, European Union’s Research and Innovation funding programme for the period 2002–2006

FP7:

Seventh framework programme, European Union’s Research and Innovation funding programme for the period 2007–2013

GCS:

Gust control system

GLAS:

Gust load alleviation system

GN:

Gauss–Newton, optimization algorithm optimized for nonlinear least squares problems

HR:

HTP root

HTP:

Horizontal tailplane

IRS:

Inertial reference system

LARS:

Load alleviation and ride smoothing

LIDAR:

Light detection and ranging

LQR:

Linear-quadratic regulator

OLGA:

Open loop gust alleviation

pdf:

Probability density function

RCAH:

Rate command attitude hold

RMS:

Root mean square

SFWA:

Smart fixed wing aircraft, integrated technology demonstrator (ITD) from the European cleanSky project

WR:

Wing root

XRF-1:

Generic long-range aircraft model designed by Airbus

\(a_z, a_{z,{\mathrm{cmd}}}\) :

Body frame vertical acceleration resp. commanded acceleration and due to all non-conservative forces, i.e., \(a_z=0 \Leftrightarrow \text {free fall}\)

\(a_{z,{\mathrm{sensor}}}\) :

Measured and low-pass filtered vertical acceleration (in body frame)

\(C_i\) :

i-th filter coefficient (\(i\in \llbracket 0,3\rrbracket\)) defining the horizontal companion representation of the third-order low-pass filter used to smooth the pilot commands, see equations (15–17)

\(C_{i,j}\) :

j-derivative of the \(C_i\) coefficient, j being either 0, Ma, or M

\(\delta _i\) :

Control surface angle, i being “elevators”, “ailerons” (symmetrical deflections), or “spoilers” (symmetrical deflections)

\(\delta _{\mathrm{pitch}}\) :

Normalized pilot pitch command (stick or control column)

\(F_z\) :

Shear force

F(s):

Cutoff filter restricting the bandwidth of the controller

g :

Gravity constant (\(\approx 9.81\) m/s)

\(\gamma _1, \gamma _2\) :

Weighting factors for the Tikhonov regularization terms

\(\varGamma _1, \varGamma _2\) :

Tikhonov matrices used to regularize the wind reconstruction problem

\(K_i\) :

Controller gain for the control surface designated by i, with i being “elevators”, “ailerons” (symmetrical deflections), or “spoilers” (symmetrical deflections)

\(l_i\) :

Lower bound on control surface deflections, i being “elevators”, “ailerons” (symmetrical deflections), or “spoilers” (symmetrical deflections)

m :

Number of measurements used for the wind reconstruction

\(M, M_{\mathrm{ref}}\) :

Vehicle mass resp. reference vehicle mass used for scheduling

\(Ma, Ma_{\mathrm{ref}}\) :

Mach number resp. reference Mach number used for scheduling

\(M_x, M_y\) :

Bending resp. torsion moment

\(\mu\)-synthesis:

Robust control technique based on the minimization of the structured singular value \(\mu\)

n :

Number of points/nodes in the wind reconstruction mesh

\(n_z, n_{z,{\mathrm{error}}}\) :

Vertical load factor (in body frame) resp. error in the vertical load factor tracking

p :

Number of parameters in the wind reconstruction model

\(P_i\) :

i-th point/node of the wind reconstruction mesh (\(i\in \llbracket 1,n\rrbracket )\)

\(\mathbb {R}, \mathbb {R}^+\) :

Set of all real numbers resp. positive real numbers (0 included)

\(\sigma _i\) :

Standard deviation for the i-th measurement

T :

Symmetrical threshold function, see Eq. (20)

\(\tau _{\mathrm{lead}}, \tau _{\mathrm{lag}}\) :

Lead resp. lag time used to define the boundaries of the reconstruction mesh, see Fig. 4

\(\theta\) :

Vector of parameters being optimized in the maximum-likelihood wind reconstruction of Sect. 2.4

\(\theta ^{[k]}\) :

Value of the parameter vector \(\theta\) at iteration k

\(\widehat{\theta }\) :

Most likely parameter vector \(\theta\) given the considered set of measurements \(\{z_i\ \vert \ i\in \llbracket 1,m\rrbracket \}\)

\(\varTheta\) :

Pitch angle

\(V_{\mathrm{TAS}}\) :

True airspeed

\(z_i, y_i(\tilde{\theta })\) :

Measurements used for the wind reconstruction resp. corresponding model outputs for given values \(\tilde{\theta }\) of the parameter vector

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Acknowledgements

Most of this work has been funded within the framework of the European CleanSky Joint Technology Initiative - Smart Fixed Wing Aircraft (Grant Agreement Number CSJU-GAM-SFWA-2008-01) and is currently being pursued within the framework of the European CleanSky2 Joint Technology Initiative - Airframe (Grant Agreement Number CS2JU-AIR-GAM-2014-2015-01 Annex 1, Issue B04, October 2nd, 2015) being part of the Horizon 2020 research and Innovation framework programme of the European Commission.

The authors would like to thank all the partners of the Smart Fixed Wing Aircraft WP1.2 for the very interesting and open discussions all along the project, Airbus for providing the XRF1 model data, as well as Thiemo Kier for his work on the benchmark model development based on these data.

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Authors and Affiliations

  1. DLR, Institute of Flight Systems, Lilienthalplatz 7, 38108, Brunswick, Germany

    Nicolas Fezans & Christoph Deiler

  2. DLR, Institute of System Dynamics and Control, Münchener Straße 20, 82234, Weßling, Germany

    Hans-Dieter Joos

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  1. Nicolas Fezans
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Fezans, N., Joos, HD. & Deiler, C. Gust load alleviation for a long-range aircraft with and without anticipation. CEAS Aeronaut J 10, 1033–1057 (2019). https://doi.org/10.1007/s13272-019-00362-9

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