Identification and verification of frequency-domain models for XV-15tilt-rotor aircraft dynamics in cruising flight

Tischler, Mark B.; Leung, Joseph; Dugan, D. C.

doi:10.2514/3.20131

Cited by 14 publications

(6 citation statements)

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“…A number of studies have presented various approaches to model the dynamics of tiltrotor aircraft. These models include detailed mathematical simulation models [3][4][5] for the purpose of real-time flight simulations, aeroelastic analytical [6][7][8] and numerical models [9,10], Linear Time Varying (LTV) model [11], and frequency domain models identified from flight test data [12][13][14]. The flight dynamics models listed in the foregoing sentence are either low-fidelity models or are only valid for discrete flight conditions and aircraft configurations.…”

Section: Introductionmentioning

confidence: 99%

Development of a quasi-linear parameter varying model for a tiltrotor aircraft

et al. 2021

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The research presented in this paper focuses on the development of a quasi-Linear Parameter Varying (qLPV) model for the XV-15 tiltrotor aircraft. The specific category of qLPV modeling technique, known as the model stitching technique, is employed to model the time-varying dynamics of XV-15 tiltrotor aircraft over the entire flight envelope. In this modeling approach, discrete linear state-space models are interpolated through lookup tables as function of scheduling parameters with the implementation of nonlinear equations of motion. The XV-15 qLPV model is configured with four scheduling parameters: altitude, nacelle incidence angle, wing flap angle and velocity. Additionally, a computational complexity analysis is presented. In particular, computational sensitivity of qLPV models configured with lookup tables to number of states and number of scheduling parameters is demonstrated. This is done to show the feasibility of real-time implementation of qLPV models with increasing fidelity (number of states) and expanding dynamic flight envelope (number of scheduling parameters).

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Section: Introductionmentioning

confidence: 99%

Development of a quasi-linear parameter varying model for a tiltrotor aircraft

et al. 2021

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“…However, the model accuracy of this modeling method is poor, especially for the aerodynamic characteristics caused by its complex structure in transition mode. Although a large number of flight test studies have been carried out, [13][14][15] Kumar 13 get the transfer function of corresponding channels obtained by means of identification, which improves the accuracy of the model. Tischler 14,15 use the frequency-domain methods to identify the open-loop dynamics of the XV-15 tiltrotor aircraft from flight tests.…”

Section: Introductionmentioning

confidence: 99%

“…Although a large number of flight test studies have been carried out, [13][14][15] Kumar 13 get the transfer function of corresponding channels obtained by means of identification, which improves the accuracy of the model. Tischler 14,15 use the frequency-domain methods to identify the open-loop dynamics of the XV-15 tiltrotor aircraft from flight tests. But they only do the identification of helicopter in hovering mode 14 and fixed-wing high-speed cruising mode.…”

Section: Introductionmentioning

confidence: 99%

“…But they only do the identification of helicopter in hovering mode 14 and fixed-wing high-speed cruising mode. 15 There are many researches on helicopter system identification, which are basically based on identification studies near a certain equilibrium point. Li 16 propose a novel algorithm combined prediction error method with artificial bee colony algorithm (PEM-ABC) to study the system identification of a small-scale unmanned helicopter in hover condition.…”

Section: Introductionmentioning

confidence: 99%

“…e �x ð1 þ e �x Þ 2 j x¼net ð2Þ j ðtÞ(15) The connection weight from input layer to hidden layer at time t is:w ð1Þ ij ðtÞ ¼ w ð1Þ ij ðt � 1Þ þ Dw ð1Þ ijðtÞ(16) …”

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A novel system identification algorithm for quad tilt-rotor based on neural network with foraging strategy

Wang¹,

Wu²,

Duan

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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Quad tilt-rotor(QTR) UAV is a nonlinear time-varying system in full flight mode. It is difficult and inaccurate to model the nonlinear time-varying system, which cannot fully reflect the problem of controlling input and system response output in the full flight mode. In order to solve the above problems, a novel neural network model was adopt to identify the nonlinear time-varying system of quad tilt-rotor in full flight mode. An adaptive learning rate algorithm based on foraging strategy is proposed based on the global error BP neural network. Corresponding to the nonlinear time-varying system, BP neural network is set as the time-invariant system structure with constant network structure and continuously changing weights at multiple times, and the nonlinear input-output relationship under the time-varying system is jointly described by fitting the network at all times. The extended Kalman filtering algorithm is used to track the network connection weights by modifying the network weights at the current moment with the input and output data at the next moment. The final identification result shows that the smaller mean square error of both only transition process and full flight mode, shows that using this optimization algorithm can well describe the input and output characteristics of the nonlinear time-varying systems. When the same network structure is adopted, no matter for transition mode or full mode, the BP optimization algorithm based on foraging strategy is better than the global BP algorithm for system identification of the full mode quad tilt-rotor. Therefore, when the BP neural network based on foraging strategy is adopted, the same network structure can be adopted to systematically identify the full mode of quad tilt-rotor by changing the weight.

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