Neural network based dynamic model and gust identification system for the Jetstream G-NFLA

Antonakis, Aristeidis; Lone, Mudassir; Cooke, Alastair

doi:10.1177/0954410016648997

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Some novel parameter identification approaches have been proposed recently. 12–15 Particularly, there is a growing interest for parameter identification via deep learning (DL) methods. 16,17 DL is a representation-learning method which can automatically extract multiple levels features of input data by training and has been successfully applied to many fields.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based inertia tensor identification of the combined spacecraft

Chu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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The identification accuracy of inertia tensor of combined spacecraft, which is composed by a servicing spacecraft and a captured target, could be easily affected by the measurement noise of angular rate. Due to frequently changing operating environments of combined spacecraft in space, the measurement noise of angular rate can be very complex. In this paper, an inertia tensor identification approach based on deep learning method is proposed to improve the ability of identifying inertia tensor of combined spacecraft in the presence of complex measurement noise. A deep neural network model for identification is constructed and trained by enough training data and a designed learning strategy. To verify the identification performance of the proposed deep neural network model, two testing set with different ranks of measure noises are used for simulation tests. Comparison tests are also delivered among the proposed deep neural network model, recursive least squares identification method, and tradition deep neural network model. The comparison results show that the proposed deep neural network model yields a more accurate and stable identification performance for inertia tensor of combined spacecraft in changeable and complex operating environments.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based inertia tensor identification of the combined spacecraft

Chu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…46 Available mathematical models for airships do not incorporate atmospheric effects and wind turbulence, 5,16 while such effects for conventional aircraft are well established. 47 It has been reported that identifying a fully parameterized grey-box model of an airship using classical identification technique suffered poor convergence, and thus a model with reduced parameters was employed. 38 In a recent work, grey-box modelling was found to yield inferior performance as compared to black-box approach for an airship.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data

Mazhar

Choudhry

Shehryar

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

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