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Manuel Pusch

Bio: Manuel Pusch is an academic researcher from German Aerospace Center. The author has contributed to research in topics: Flight control surfaces & Aeroelasticity. The author has an hindex of 8, co-authored 22 publications receiving 145 citations.

Papers
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Proceedings ArticleDOI
06 Jan 2019
TL;DR: The paper presents the control design approaches for the European research project FLEXOP, which aims to develop and apply active flutter suppression and load alleviation techniques on an unmanned flying aircraft demonstrator.
Abstract: The paper presents the control design approaches for the European research project FLEXOP. The ultimate goal is to develop and apply active flutter suppression and load alleviation techniques on an unmanned flying aircraft demonstrator. Due to the flexible wing of the aircraft new challenges rise for the control design: the traditional rigid body (baseline) control loops have to be augmented with flutter control laws. In our approach, the controllers are designed based on a dynamicalmodel, which is briefly discussed first. Details of the baseline control design, as well as the two different flutter suppression algorithms are discussed in the paper. Hardware-in-the-Loop testing of the controllers are reported before the first test flights of the aircraft.

21 citations

Journal ArticleDOI
05 Mar 2019
TL;DR: The model-based flight control system design for a highly flexible flutter demonstrator, developed in the European FLEXOP project, is presented and is verified in an extensive simulation campaign using a high fidelity simulator.
Abstract: 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.

20 citations

Proceedings ArticleDOI
08 Jan 2018
TL;DR: A novel approach is presented for designing H2-optimal blending vectors for the control of individual aeroelastic modes by jointly compute the interdependent input and output blending vectors, where an explicit mode decoupling can be considered.
Abstract: For flexible aircraft, it is often required to control individual aeroelastic modes which are lightly damped or even unstable. In order to achieve a maximum controller performance, a large number of measurements and control surfaces is required, which in turn complicates controller design. Blending control inputs and measurement outputs, individual aeroelastic modes can be isolated efficiently and hence controlled by a single input single output controller. In this paper, a novel approach is presented for designing H2-optimal blending vectors for the control of individual aeroelastic modes. An efficient algorithm is derived to jointly compute the interdependent input and output blending vectors, where an explicit mode decoupling can be considered. The effectiveness of the proposed approach is proven by designing a gust load alleviation system for a flexible aircraft with distributed flaps and measurements.

20 citations

Proceedings ArticleDOI
05 Jan 2020
TL;DR: The paper details the research and corresponding implementation and testing steps of the FLEXOP demonstrator aircraft, which is built to validate the mathematical modelling, flight control design and implementation side of active flutter mitigation within the EU funded project.
Abstract: The paper details the research and corresponding implementation and testing steps of the FLEXOP demonstrator aircraft. Within the EU funded project an unmanned demonstrator aircraft is built to validate the mathematical modelling, flight control design and implementation side of active flutter mitigation. In order to validate the different methods and tools developed in this project, a flight test campaign is planned, in which the design and manufacturing of stiff wings (-0), are compared with very flexible wings (-1) with active flutter control, to see the overall benefit vs. risk of such technology. The mathematical models of the aircraft are first developed using FEM and CFD tools, what are later reduced by model order reduction techniques. The high-fidelity models are updated using Ground Vibration Test results. Manufacturing tolerances and variations in aircraft parameters are captured by systematic modelling of parametric and dynamic uncertainties. Both the simulation environment and the control design framework use different modelling fidelity, what are described within the paper. Reduced models are developed using two distinctive methods, respecting the control design needs: top-down balanced LPV reduction and bottom-up structure preservingmethods. Based on the reduced order models various control design techniques have been elaborated by the consortium partners. In particular DLR developed and implemented a modal control method using H2 optimal blends for inputs and outputs. University of Bristol developed structured H-infinity optimal control methods, while SZTAKI proposed a worst-case gain optimal method structured controller synthesismethod handling parametric and complex uncertainties. After the brief introduction of hardware-in-the-loop test setup and the description of mission scenarios the implementation issues of the baseline and flutter controllers are discussed. DLR and SZTAKI flutter controllers are evaluated in a hybrid software/ hardware-in-the-loop test setup as at this stage of development the latter can not tolerate the estimated delay of the hardware system but their comparison is advantageous before future developments. Recommendations on active flutter mitigation methods are given based on the experience of synthesis and implementation of these controllers. Flight test results will follow these experiments, once the flight testing of the flutter wing commences.

16 citations


Cited by
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01 Nov 1981
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

1,829 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a lecture series on aero-acoustic problems, including jet noise, turbulent boundary layers and fan-tip leakage, with references and bibliographies for further study.
Abstract: plication of these to some typical aeroacoustic problems, including jet noise, turbulent boundary layers and fan-tip leakage. These proceedings are all well written, clearly presented and easy to follow. The presenters are all active researchers and engineers, and, consequently, the information is topical and current. The long lists of references and bibliographies provide an excellent starting point for further study. The preface to the volume claims, rather boldly, that all sound production mechanisms are addressed. This is not the case, as some aeroengine noise sources, such as engine handling bleed valves and broad-band combustion noise, are missing. But these are minor omissions, and it is fair to say that all the major sources are covered. This lecture series would indeed constitute an excellent introduction for engineers, scientists and PhD students entering the field. Craig J. Mead, CEng, MRAeS Aero Acoustics

63 citations

Journal ArticleDOI
TL;DR: In this article, it is current practice to operate aircraft well below their open-loop flutter speed in a stable and controllable manner, in order to avoid an unstable oscillation caused by the interaction of aerodynamics and structural dynamics.
Abstract: Flutter is an unstable oscillation caused by the interaction of aerodynamics and structural dynamics. It is current practice to operate aircraft well below their open-loop flutter speed in a stable...

26 citations

Proceedings ArticleDOI
06 Jan 2019
TL;DR: The paper presents the control design approaches for the European research project FLEXOP, which aims to develop and apply active flutter suppression and load alleviation techniques on an unmanned flying aircraft demonstrator.
Abstract: The paper presents the control design approaches for the European research project FLEXOP. The ultimate goal is to develop and apply active flutter suppression and load alleviation techniques on an unmanned flying aircraft demonstrator. Due to the flexible wing of the aircraft new challenges rise for the control design: the traditional rigid body (baseline) control loops have to be augmented with flutter control laws. In our approach, the controllers are designed based on a dynamicalmodel, which is briefly discussed first. Details of the baseline control design, as well as the two different flutter suppression algorithms are discussed in the paper. Hardware-in-the-Loop testing of the controllers are reported before the first test flights of the aircraft.

21 citations

Journal ArticleDOI
05 Mar 2019
TL;DR: The model-based flight control system design for a highly flexible flutter demonstrator, developed in the European FLEXOP project, is presented and is verified in an extensive simulation campaign using a high fidelity simulator.
Abstract: 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.

20 citations