Structured Control Design for a Highly Flexible Flutter Demonstrator

The model-based flight control system design for a highly flexible flutter demonstrator, developed in the European FLEXOP project, is presented. The flight control system includes a baseline controller to operate the aircraft fully autonomously and a flutter suppression controller to stabilize the unstable aeroelastic modes and extend the aircraft’s operational range. The baseline control system features a classical cascade flight control structure with scheduled control loops to augment the lateral and longitudinal axis of the aircraft. The flutter suppression controller uses an advanced blending technique to blend the flutter relevant sensor and actuator signals. These blends decouple the unstable modes and individually control them by scheduled single loop controllers. For the tuning of the free parameters in the defined controller structures, a model-based approach solving multi-objective, non-linear optimization problems is used. The developed control system, including baseline and flutter control algorithms, is verified in an extensive simulation campaign using a high fidelity simulator. The simulator is embedded in MATLAB and a features non-linear model of the aircraft dynamics itself and detailed sensor and actuator descriptions.

[1]  Pierre Apkarian,et al.  Nonsmooth H∞ synthesis , 2005, IEEE Trans. Autom. Control..

[2]  Manuel Pusch Aeroelastic Mode Control using H2-optimal Blends for Inputs and Outputs , 2018 .

[3]  Daniel Ossmann,et al.  Aeroservoelastic Modeling and Analysis of a Highly Flexible Flutter Demonstrator , 2018, 2018 Atmospheric Flight Mechanics Conference.

[4]  Peter J Seiler,et al.  Flight testing flutter suppression on a small flexible flying-wing aircraft , 2018 .

[5]  Christian Breitsamter,et al.  Aircraft Design and Testing of FLEXOP Unmanned Flying Demonstrator to Test Load Alleviation and Flutter Suppression of High Aspect Ratio Flexible Wings , 2019, AIAA Scitech 2019 Forum.

[6]  Mirko Hornung,et al.  Mission and Aircraft Design of FLEXOP Unmanned Flying Demonstrator to Test Flutter Suppression within Visual Line of Sight , 2017 .

[7]  Daniel Ossmann,et al.  Baseline Flight Control System Design for an Unmanned Flutter Demonstrator , 2019, 2019 IEEE Aerospace Conference.

[8]  Brian P. Danowsky Flutter Suppression of a Small Flexible Aircraft using MIDAAS , 2017 .

[9]  Peter J Seiler,et al.  Robust control design for active flutter suppression , 2016 .

[10]  Bálint Vanek,et al.  Aircraft Aeroservoelastic Modelling of the FLEXOP Unmanned Flying Demonstrator , 2019, AIAA Scitech 2019 Forum.

[11]  Andres Marcos,et al.  Aeroelastic modeling and stability analysis: A robust approach to the flutter problem , 2017 .

[12]  Mirko Hornung,et al.  Designing an UAV Propulsion System for Dedicated Acceleration and Deceleration Requirements , 2017 .

[13]  Matthias Heller,et al.  In-Flight Validation of a Robust Flight Controller Featuring Anti-Windup Compensation , 2018 .

[14]  Bálint Vanek,et al.  Control oriented reduced order modeling of a flexible winged aircraft , 2018, 2018 IEEE Aerospace Conference.

[15]  Frank L. Lewis,et al.  Aircraft control and simulation: Dynamics, controls design, and autonomous systems: Third edition , 2015 .

[16]  Daniel Ossmann,et al.  $\mathcal{H}_{2}$ -Optimal Blending of Inputs and Outputs for Modal Control , 2020, IEEE Transactions on Control Systems Technology.

[17]  Pierre Apkarian,et al.  Parametric Robust Structured Control Design , 2014, IEEE Transactions on Automatic Control.

[18]  P. Apkarian,et al.  Nonsmooth H ∞ synthesis , 2005 .

[19]  Daniel Ossmann,et al.  Robust autopilot design for landing a large civil aircraft in crosswind , 2018, Control Engineering Practice.

[20]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[21]  Pierre Apkarian,et al.  Multi-model, multi-objective tuning of fixed-structure controllers , 2014, 2014 European Control Conference (ECC).

[22]  Duane T. McRuer,et al.  Aircraft Dynamics and Automatic Control , 1973 .

[23]  Bálint Vanek,et al.  Model reduction for LPV systems based on approximate modal decomposition , 2016, ArXiv.