Comparing different approaches for modeling the vertical motion of the EC 135

Seher-Weiß, Susanne

doi:10.1007/s13272-015-0150-7

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The feed-forward controller provides basic response decoupling and improves the response quickness. A 11-DoF helicopter dynamic model which is derived by system identification in time domain is the base for the feed-forward controller [18]. The feedback controller compensates differences between commanded and measured values due to modeling deficiencies and other disturbances.…”

Section: Advanced Flight Control Modes At Dlrmentioning

confidence: 99%

Helicopter augmented control laws for ship deck landing: HACLAS ONERA/DLR Joint Team

Kalra,

Binet

2024

CEAS Aeronaut J

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Different types of control laws are implemented, tested and compared for maritime operations particularly ship deck landing maneuvers at the flight simulation facilities at both DLR (German Aerospace Center) and ONERA (The French Aerospace Lab). At DLR, “classical” cyclic and collective stick flight controls were used during the piloted simulator trials while active side-sticks were operated at ONERA. A joint maritime scenario for ship deck landing in the simulation environments of both institutes is presented. Test methodologies and assessment techniques to evaluate the ship deck landings are harmonized based on different criteria such as quantitative measures and handling qualities (HQ) ratings to analyze the developed control laws. Simulation results based on pilot studies for an EC135 in the DLR simulator and an EC225 at ONERA are presented.

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Section: Advanced Flight Control Modes At Dlrmentioning

confidence: 99%

Helicopter augmented control laws for ship deck landing: HACLAS ONERA/DLR Joint Team

Kalra,

Binet

2024

CEAS Aeronaut J

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“…All these different modeling formulations are applied to ACT/FHS flight-test data and compared in Ref. [76]. The hybrid model provided a good match for frequencies up to 15 rad/s, which is sufficient for many flight dynamics and control applications.…”

Section: Inflow/coningmentioning

confidence: 99%

“…Fig 16. Simulated frequency responses for a z ∕δ col using different models (ACT/FHS in hover), adapted from Ref [76]…”

mentioning

confidence: 99%

Retrospective and Recent Examples of Aircraft and Rotorcraft System Identification at DLR

Deiler

Mönnich

Seher-Weiß

et al. 2023

Journal of Aircraft

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Aircraft system identification has a five-decades-long tradition at German Aerospace Center (DLR). Over the last two decades, the research covered various topics related to system identification of fixed- and rotary-wing aircraft, nonconventional applications and atmospheric effects, the development of new flight-test procedures for system identification purposes, and specific aircraft model enhancements and corresponding parameter estimation. Comprehensive tools were developed that support this research and can be applied to a variety of different problems and types of vehicles. The paper starts with a short description of the different system identification methods used at DLR and the corresponding tools. The discussion of flight-test procedures and maneuver design as well as sensor fusion and flight-path reconstruction provides information on how to optimize the flight tests for system identification and to arrive at a consistent flight-test database. The examples for fixed-wing aircraft provide information on identification including abnormal conditions such as icing and interaction with atmospheric disturbances as well as modeling of structural mechanics and loads. The identification of high-order rotorcraft models that account for rotor and engine dynamics and even structural modes is discussed, and the identification of rotor mast moments as well as the identification of non-physics-based models and their integration into physics-based models are also covered. A final section shows that system identification can also be used to derive models for gyroplanes and parachutes as well as to derive control equivalent turbulence input models and to estimate complex wind field geometries. Thus, a broad overview of possible applications of system identification is given.

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“…In the past, different modeling aspects of ACT/FHS system identification have been described in separate papers (see Refs. 13,14). In this paper, these results are reevaluated with respect to the requirements of model interpolation and model inversion.…”

Section: Introductionmentioning

confidence: 99%

ACT/FHS System Identification Including Rotor and Engine Dynamics

Seher-Weiß¹

2019

j am helicopter soc

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At the German Aerospace Center (DLR) Institute of Flight Systems, models of the Active Control Technology/FlyingHelicopter Simulator (ACT/FHS), an EC135 with a fly-by-wire/light flight control system, are needed for control law development and simulation. Thus, models are sought that cover the whole flight envelope and are valid over a broad range of frequencies. Furthermore, if the models are to be used in the feedforward loop of the model following the control system, they have to be invertible and thus should not have any positive transmission zeros. For rotor flapping, the explicit formulation with flapping angles was modified slightly to avoid positive transmission zeros. For the regressive lead-lag, a simple model formulation was found that needs only one dipole with two states. The engine dynamics were first modeled separately and then coupled to the body/rotor model. The final integrated model has 17 states and yields a good match for frequencies up to 30 rad/s. All system identification was performed using the maximum likelihood method in the frequency domain. A, B, C, D state-space representation of dynamic model a x , a y , a z body-fixed linear accelerations, m/s 2 C Lα blade lift curve slope, 1/rad C T thrust coefficient, = T /[ρπR 2 ( R) 2 ] C 0 inflow constant c rotor blade chord, m D δ lon , D δ lat control derivatives of the lead-lag dipole E .. engine model parameters e hinge offset, m g acceleration of gravity, m/s 2 H transfer function matrix I identity matrix I β blade flapping moment of inertia, kg m 2 K β flapping stiffness, Nm/rad K θ 0 control gain, rad/% L .. , M.., N.. moment derivatives Lf .. , Mf .. flapping moment derivatives m aircraft mass, kg p, q, r roll, pitch, and yaw rates, rad/s * Corresponding author; Nomenclature

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Comparing different approaches for modeling the vertical motion of the EC 135

Cited by 4 publications

References 6 publications

Helicopter augmented control laws for ship deck landing: HACLAS ONERA/DLR Joint Team

Helicopter augmented control laws for ship deck landing: HACLAS ONERA/DLR Joint Team

Retrospective and Recent Examples of Aircraft and Rotorcraft System Identification at DLR

ACT/FHS System Identification Including Rotor and Engine Dynamics

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