Influence of high-order dynamics on helicopter flight-control systembandwidth

Chen, Robert T. N.; Hindson, William S.

doi:10.2514/3.20089

Cited by 30 publications

(7 citation statements)

References 3 publications

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“…These dynamics were implemented through the analytical model proposed by Chen and Hindson (Ref. 5). In this model the coning/inflow dynamic equations provide three more states: the inflow ν, the coning angle β 0 and the coning rate β0 .…”

Section: Extended State-space Identificationmentioning

confidence: 99%

Frequency Domain System Identification of a Robinson R44 in Hover

Geluardi

Nieuwenhuizen

Venrooij

et al. 2018

j am helicopter soc

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The civil light helicopter domain has not fully benefited yet from the advantages system identification methods can offer. The aim of this paper is to show that system identification methods are mature enough to be successfully implemented in the civil helicopter domain. To achieve this goal, a Robinson R44 Raven II is identified in this work. The identification focuses on the hover trim condition. A lean frequency domain identification method is adopted. Furthermore, a new procedure is proposed to limit the sensitivity of the state-space minimization algorithm to initial parametric values and bounds. The resulting state-space model presents good predictive capabilities and is able to capture high frequency rotor-body dynamics. The model is also validated with the help of a helicopter pilot by performing closed-loop control task maneuvers in the MPI CyberMotion Simulator. The g gravitational acceleration, [m/s 2 ] s Laplace transform variable φ, θ, ψ fuselage angular attitude (roll, pitch, yaw) earth-fixed coordinates, [rad] β 0 , β 1c , β 1s rotor coning, longitudinal and lateral flapping angles, [rad] τ f rotor flap time constant, [s] ν rotor inflow velocity, [m/s] ν0 trim inflow ratio γ Lock number ξ, ω damping and natural frequency of a second order system η Ct integrated perturbation thrust coefficient σ rotor solidity ρ atmospheric density, [Kg/m 3 ] δ lat , δ lon , δ ped , δ col helicopter control inputs (lateral cyclic, longitudinal cyclic, pedals rudder, collective lever), [deg] Ω rotor rotation speed, [rad/s] ω frequency, [rad/s]

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Section: Extended State-space Identificationmentioning

confidence: 99%

Frequency Domain System Identification of a Robinson R44 in Hover

Geluardi

Nieuwenhuizen

Venrooij

et al. 2018

j am helicopter soc

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“…The regressing flapping mode is the most relevant when considering the effect of rotor dynamics on handling characteristics, it is the lowest frequency mode of the three, and it has a tendency to couple into the fuselage modes. [6][7][8][9][10][11][12] Additionally for helicopter directional axis control, blade lead-lag dynamics ought to be considered for control system design. 13 In particular it is well known that blade lead-lag produces increased phase lag at high frequency, in the same frequency range where flapping effects occur, 10 and that control rate gains are primarily limited by lead-lagbody coupling.…”

Section: Ia Backgroundmentioning

confidence: 99%

Small-Scale Helicopter Blade Flap-Lag Equations of Motion for a Flybarless Pitch-Lag-Flap Main Rotor

Taamallah

2011

AIAA Modeling and Simulation Technologies Conference

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We derive the coupled flap-lag equations of motion, for a small-scale helicopter flybarless rotor, in the case of a rigid articulated Pitch-Lag-Flap (P-L-F) hinge arrangement. The (P-L-F) sequence is indeed much more useful for modeling the rotor dynamics of R/C helicopters. The derivation, obtained by the Lagrangian method, allows for hinge springs and viscous dampers, for both ClockWise (CW) and CounterClockWise (CCW) rotating main rotors, while keeping all hinges physically separated. Although the equations are valid for small flap, lag, and pitch angles, the exact tangential and perpendicular blade velocity expressions have been retained, hence full coupling between vehicle and blade dynamics is modeled. Additionally the paper reviews all assumptions made in deriving the model, i.e. structural, aerodynamics, and dynamical simplifications. The model has been compared with an equivalent FLIGHTLAB R rotor model, and simulation results show that the model validity is good to very good in hover and low speed, and fair to good at high speed.

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“…High-bandwidth flight control systems with high feedback gains can improve turbulence alleviation; however, the systems may be destabilized by the rotor dynamics if gains are set too high. 1–3 Study by Zhao and Curtiss 4 indicated that the instability of helicopter in the presence of rate or attitude feedback is often related to the lightly damped lag modes. Flight tests of RAH-66 5 and UH-60A 6 helicopters showed that both low- and high-frequency lag modes can be an issue when designing advanced control laws.…”

Section: Introductionmentioning

confidence: 99%

Rotor-state feedback control design to improve helicopter turbulence alleviation in hover

Chen

Pan

2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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A helicopter flight control system with rotor-state feedback to improve turbulence alleviation in hover is presented. First, a flight dynamic model coupled with turbulence model is developed and validated. Then, an integrated control strategy with a rotor-state feedback control law is proposed based on the baseline control system. The feedback gains of body and rotor states are designed in synergy to improve turbulence alleviation in the interested frequency range of handling qualities. Subsequently, the effects of the rotor-state feedback gains on both the stability of rotor dynamics and helicopter turbulence alleviation are analyzed in detail. Finally, the effectiveness of the integrated control system is evaluated with linear analysis in frequency domain and nonlinear simulation in time domain. The results indicate that with the rotor-state feedback control law integrated into the control system, the helicopter turbulence alleviation in the interested frequency range is improved with less degradation in helicopter stability margins, and the roll and pitch rate responses of helicopter to turbulence, measured with Root-Mean-Square (RMS) values, are reduced by more than 50% and 35% respectively.

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Influence of high-order dynamics on helicopter flight-control systembandwidth

Cited by 30 publications

References 3 publications

Frequency Domain System Identification of a Robinson R44 in Hover

Frequency Domain System Identification of a Robinson R44 in Hover

Small-Scale Helicopter Blade Flap-Lag Equations of Motion for a Flybarless Pitch-Lag-Flap Main Rotor

Rotor-state feedback control design to improve helicopter turbulence alleviation in hover

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