Neural Network Representation of External Tilt-Rotor Noise

Kottapalli, Sesi; Kitaplioglu, Cahit

doi:10.4050/jahs.47.109

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Hyland 22 develop adaptive neural identification algorithms that are able to minimize the influences of extrinsic noise on the quality of the identified model. Kottapalli 23 find that the neural networks are successfully used to assess the quality of the noise data and to represent the complete database as well as to predict tilt-rotor noise using the minimal amount of input data. Horn 24 develop a method for using neural networks to provide predictive flight envelope limit information.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

Malki¹,

Kottapalli²,

Langari³

2003

44th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

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