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

Ji, Honglei; Chen, Renliang; Li, Pan

doi:10.2514/1.c034189

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…The turbulence signal must have the same frequency spectrum as the PSD of the Dryden wind. For this reason, we should use the following filters defined by (3) [33]:…”

Section: The Dryden Wind Turbulencementioning

confidence: 99%

Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control

Chater

Housny

Fadil

2022

IJECE

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<span>When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hidden layer deep feedforward network (DFN), where the one-step secant algorithm is chosen for initializing the DFN parameters. All the design steps of the proposed adaptive controller are described. The multidimensional particle swarm optimization (PSO) algorithm is used for tuning the DFN parameters. Then, using two simulation efficiency tests, a comparison between the proposed PSO-based APID-DFN, the (non-optimized) APID-DFN, the feedforward network APID, and the fixed-parameter PID controllers proves much efficiency of the proposed adaptive controller. The results illustrate that the proposed PSO-based APID-DFN controller can ensure good quadrotor system stabilization and achieve minimum overshoot and faster convergence speed for all quadrotor motions. Thus, the proposed control strategy could be considered an additional intelligent method-based adaptive control for unmanned aerial vehicles.</span>

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“…The turbulence signal must have the same frequency spectrum as the PSD of the Dryden wind. For this reason, we should use the following filters defined by (3) [33]:…”

Section: The Dryden Wind Turbulencementioning

confidence: 99%

Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control

Chater

Housny

Fadil

2022

IJECE

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“…The atmospheric turbulence model mainly includes the engineering simulation method and numerical simulation method. The engineering simulation method is mainly based on the Dryden and Von Karman model (Mouyon et al., 2002; Ji et al., 2017) established by the Monte Carlo method, which is mainly used in the construction of the real-time flight environment simulation platform of the carrier aircraft and the aircraft control platform based on MATLAB Simulink. It is not able to simulate or analyse the impact of atmospheric turbulence on the deflection deformation of the wing.…”

Section: Influence Of External Disturbance On Wing Deflectionmentioning

confidence: 99%

Research on a modeling method of wing deformation under the influence of separation and compound multi-source disturbance

et al. 2021

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An aircraft wing is the carrier of imaging payload (interferometric synthetic aperture radar (SAR) or array SAR) of a high-resolution aerial remote sensing system, and high-precision estimation of wing deformation is the key. There are two main traditional modelling methods for wing deformation, namely stochastic theory modelling and material mechanics modelling only dealing with single disturbance, of which the model parameters are derived from empirical values. Aiming at the complex multi-source disturbance of an aircraft wing, this paper separately probes the influence of external disturbance (air disturbance) and internal disturbance (engine vibration) based on the real-time observation of sensors and classifies the wing deformation on the basis of auto-regressive (AR) modelling for parameter identification. With the authentic flight data of a certain types of aircraft, the experimental analysis shows that the wing deformation under the influence of engine vibration is the 14th-order AR model, and the wing deformation under the influence of turbulence is the fifth-order AR model. Meanwhile, this paper also provides an experimental verification idea for the wing deflection modelling built on the second- or third-order Markov model.

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“…In addition, handling quality standards are widely used to evaluate the interference of other factors on helicopters. Ji [12] used a high-precision turbine model to assess the impact of aerodynamic changes in helicopter components in distributed atmospheric turbulence on handling quality. Yuan [13,14] assessed the effect of aerodynamic interference on the handling quality of tractor aircraft using the uncertainty quantification method.…”

Section: Introductionmentioning

confidence: 99%

Influence of Engine Dynamic Characteristics on Helicopter Handling Quality in Hover and Low-Speed Forward Flight

Wei,

Chen,

Yuan

et al. 2023

Aerospace

Self Cite

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This study assesses the influence of engine dynamic characteristics on helicopter handling quality during hover and low-speed forward flight. First, we construct the helicopter–engine coupling model (HECM) based on the power-matching relationship between the engine and the rotor. The impact of the engine is evaluated by comparing HECM with a helicopter model without the engine. To assess the engine’s influence quantitatively, we consider torque response, height response, and collective–yaw coupling characteristics in ADS-33E-PRF handling quality criteria. The results reveal that the engine power output lag can deteriorate the helicopter’s torque and height response handling quality rate (HQR). After the increase in helicopter mass, the torque HQR caused by engine influence improved, and the altitude HQR further deteriorated. The engine dynamic characteristics can also reverse the yaw rate, decreasing collective–yaw coupling HQR. As the helicopter’s flight speed increased, the engine’s impact on the yaw rate increased by 41.8%. This study can provide valuable insight into the effects of engine dynamic characteristics on helicopter handling quality and offer a reference for the design of helicopter–engine coupling control laws.

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

Cited by 10 publications

References 9 publications

Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control

Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control

Research on a modeling method of wing deformation under the influence of separation and compound multi-source disturbance

Influence of Engine Dynamic Characteristics on Helicopter Handling Quality in Hover and Low-Speed Forward Flight

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