Development of EC 135 turbulence models via system identification

Seher-Weiß, Susanne; Gruenhagen, Wolfgang Von

doi:10.1016/j.ast.2011.09.008

Cited by 25 publications

(21 citation statements)

References 3 publications

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“…For the ACT/FHS the modeling variant with the dipoles at the input was used, where two second-order dipoles act on the longitudinal and lateral cyclic inputs as described in Ref. 19. The corresponding state equations are ⎛…”

Section: Extension To Two Inputs and Two Outputsmentioning

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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Section: Extension To Two Inputs and Two Outputsmentioning

confidence: 99%

ACT/FHS System Identification Including Rotor and Engine Dynamics

Seher-Weiß¹

2019

j am helicopter soc

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“…Instead, the Control Equivalent Turbulence Input (CETI, Ref. [27]) method was adopted. The CETI approach uses a similar technique to the von Karman method, in that it passes white noise through appropriately designed filters to generate disturbance signals.…”

Section: Atmospheric Disturbancesmentioning

confidence: 99%

“…The structure of the CETI models used in this study was adopted from Ref. [27]. For example, the structure of the CETI model for the longitudinal cyclic is shown in Equation 2 below:…”

Section: Atmospheric Disturbancesmentioning

confidence: 99%

“…Initial parameter settings for the turbulence filters were also drawn from Ref. [27]. However, as the PAV is considered to be a small air vehicle (mass  500kg), and the aircraft used to generate the parameters was considerably larger (an EC-135 helicopter, mass  2800kg), the parameter settings were subjectively tuned using the opinions of a current light helicopter pilot to create a turbulence response that was considered appropriate for a vehicle of the size of the PAV.…”

Section: Atmospheric Disturbancesmentioning

confidence: 99%

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Methods to Assess the Handling Qualities Requirements for Personal Aerial Vehicles

Perfect

Jump

White

2015

Journal of Guidance, Control, and Dynamics

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This paper describes the development of a methodology to assess the handling qualities requirements for Personal Aerial Vehicles (PAVs). It is anticipated that a PAV would be flown by a 'flight-naïve' pilot who has received less training than is typically received by today's general aviation private pilots. The methodology used to determine handling requirements for a PAV cannot therefore be based entirely on existing best practicethe use of highly experienced test pilots in a conventional handling assessment limits the degree to which results apply to the flight-naïve pilot. This paper describes an alternative set of methods based on both the subjective and objective analysis of performance and workload of flight-naïve pilots in typical PAV tasks. A highly reconfigurable generic flight dynamics simulation model that has been used to validate the methodology is also described. Results that highlight the efficacy of the various methods are presented and their suitability for use with flight naïve pilots demonstrated. Nomenclature  lat = Lateral stick input [-1:1]  lon = Longitudinal stick input [-1:1]  lon,gust = Longitudinal stick input component from gust [-1:1]  lon,pilot = Longitudinal stick input component from pilot [-1:1]  lon,total = Longitudinal stick input from gust and pilot combined [-1:1]  cmd = Commanded Bank Angle [rad]  lat = Roll Response Natural Frequency [rad/s]

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“…Atmospheric turbulence would result in undesired helicopter motions and high pilot workloads, sometimes even pushing the aircraft to its operational limits. For these reasons, a variety of turbulence models, from the physics-based turbulence modeling methods [1][2][3][4][5] to the flight-based control equivalent turbulence input approach [6][7][8], have been developed for the prediction of helicopter response to atmospheric turbulence. Among the various models, the current distributed turbulence model developed by Ji et al [5] is well suited for helicopter flight simulation and handling quality analysis in lowaltitude and low-speed flight conditions against the flight-test data with high computational efficiency.…”

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confidence: 99%

Analysis of Helicopter Handling Quality in Turbulence with Recursive Von Kármán Model

Chen

2017

Journal of Aircraft

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A recursive von Kármán turbulence model is developed and integrated into the current distributed turbulence model for helicopter flight simulation and handling quality analysis in atmospheric turbulence. First, high-order filters are developed with rational expressions to approximate the von Kármán spectra. Recursive algorithms for modeling the von Kármán turbulence are derived by discretizing the filters with the Tustin transform. Then, the recursive von Kármán turbulence model is used to replace the Dryden model in the current distributed turbulence model for helicopter flight simulation in atmospheric turbulence. The results show that the spectra of the recursive von Kármán turbulence have an excellent agreement with the theoretical von Kármán spectra, and they are valid over a normalized frequency range up to 400 rad, which is suitable for helicopter handling quality analysis in atmospheric turbulence with low flight speed and large turbulence scale lengths. The present turbulence model significantly extends the capability of helicopter handling quality analysis to the higher-altitude and lower-velocity flight conditions compared with the current distributed turbulence model. Nomenclature H u;v;w s = shaping filters of turbulence velocity components to random inputs L u;v;w = scale lengths for turbulence spectra, m p; q; r = angular rates about body axes, deg ∕s t = time, s u, v, w = translational velocity components along body axes, m∕s V = aircraft flight speed, m∕s x, x i = random inputs of continuous and discrete shaping filters y, y i = turbulence outputs of continuous and discrete shaping filters γ u;v;w = V∕L u;v;w , 1∕s Δt = sampling period, s Δu, Δv, Δw = turbulence velocity components, m∕s σ u;v;w = standard deviations of turbulence velocity components, m∕s Φ, Θ, Ψ = aircraft Euler angles, rad Φ X ω, Φ DX ω = power spectral densities of continuous and discrete random inputs Φ Y ω, Φ DY ω = power spectral densities of continuous and discrete turbulence outputs Φ u;v;w ω = power spectral densities of turbulence velocity components ω; ω D = continuous and discrete angular frequencies, rad∕s ω D max = Nyquist frequency, rad∕s

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Development of EC 135 turbulence models via system identification

Cited by 25 publications

References 3 publications

ACT/FHS System Identification Including Rotor and Engine Dynamics

ACT/FHS System Identification Including Rotor and Engine Dynamics

Methods to Assess the Handling Qualities Requirements for Personal Aerial Vehicles

Analysis of Helicopter Handling Quality in Turbulence with Recursive Von Kármán Model

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