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Proceedings ArticleDOI

Estimation of nonlinear airship parameters using modular neural network

TL;DR: The estimated nonlinear airship parameters are found to be consistent with the DATCOM parameters which were used for open-loop simulation in data generation phase and validates the proposed methodology and could be extended to estimateAirship parameters from real flight data.
Abstract: The prime objective of this work is to estimate stability and control derivatives of an airship. The complete, nonlinear mathematical model of aerial vehicles has its aero model as a nonlinear function of angle of attack. This along with the necessity for an exhaustive dataset complicates the estimation procedure. In this work, data are generated by simulating the mathematical model of airship for different trim conditions obtained from continuation analysis. A modular neural network is then trained using back-propagation and Adam optimization algorithm for each aerodynamic coefficient separately. The estimated nonlinear airship parameters are found to be consistent with the DATCOM parameters which were used for open-loop simulation in data generation phase. This validates the proposed methodology and could be extended to estimate airship parameters from real flight data.
Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors present a survey on the integration of blockchain with aerial communications (BAC) and their current security issues, blockchain and its advantages, the feasibility and opportunity of applying the blockchain to resolve the current security issue in aerial communication networks.

21 citations

Proceedings ArticleDOI
21 Jun 2022
TL;DR: In this paper , a ducted-fan tiltrotor unmanned aerial vehicle (UAV) with vertical takeoff and landing capability has been proposed, which has three flight modes: helicopter mode, transitional mode and fixed-wing mode.
Abstract: This paper proposes a novel ducted-fan tiltrotor unmanned aerial vehicle (UAV) with vertical takeoff and landing capability. Firstly, the geometry of the UAV with three views is given. The proposed UAV has three flight modes: helicopter mode, transitional mode and fixed-wing mode. Then, to guarantee that the overall design meets the system specifications, the Digital DATCOM program is utilized to calculate both the stability and control derivatives of the UAV under fixed-wing mode. Finally, numerical simulation results are given to demonstrate the changes of the stability and control derivatives with respect to different cruise speeds and angle of attacks.

1 citations

References
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Proceedings Article
01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

111,197 citations


"Estimation of nonlinear airship par..." refers methods in this paper

  • ...In this case, Adam optimization algorithm [14] is used instead of the classical stochastic gradient-descent procedure because it combines best properties of AdaGrad and RMSProp algorithms and can handle sparse gradients on noisy algorithms....

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Journal ArticleDOI
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.

18,794 citations


"Estimation of nonlinear airship par..." refers background in this paper

  • ...The main advantage of using FFNN is its ability to map any nonlinear function [10]....

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  • ...any nonlinear function can be computed and learnt by the network [10]....

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Journal ArticleDOI
TL;DR: Examples of the use of multi-layer feed-forward neural networks for prediction of carbon-13 NMR chemical shifts of alkanes is given and advantages and disadvantages of multilayer feed- forward neural networks are discussed.

1,206 citations


"Estimation of nonlinear airship par..." refers background in this paper

  • ...For parameter estimation of airship, the input variable to the module or FFNN is α and the response variable is unknown nonlinear airship parameter from (4)....

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  • ...In each modular neural network, modules or independent FFNNs are integrated with a dot product layer to output aerodynamic coefficient....

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  • ...The main advantage of using FFNN is its ability to map any nonlinear function [10]....

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  • ...FFNNs are composed of group of neurons that are arranged into input, hidden and output layers....

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  • ...Thus, each FFNN is composed of 1 node in input layer, 5 nodes in hidden layer and 1 node in output layer....

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BookDOI
01 Jan 2004
TL;DR: The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation and enables the user to implement and execute the programs himself to gain first hand experience of the estimation process.
Abstract: Modelling and Systems Parameter Estimation for Dynamic Systems presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation. The material is presented in a way that makes for easy reading and enables the user to implement and execute the programs himself to gain first hand experience of the estimation process.

147 citations


"Estimation of nonlinear airship par..." refers background in this paper

  • ..., with τ as the sampling interval [15]....

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Journal ArticleDOI
TL;DR: Robustness of the method with respect to measurement noise is demonstrated by its applicability to simulated data with pseudomeasurement noise, and to real-e ight data.
Abstract: A recently proposed method (christened ‘ ‘ the Delta method’ ’) of estimating aircraft parameters from e ight data using feed-forward neural networks is applied for the extraction of lateral ‐directional parameters from simulated as well as real-e ight data. The neural network is trained using aircraft motion and control variables as the network inputs and aerodynamic coefe cients as the network outputs; the trained network is used to predict aerodynamic coefe cients for a suitably modie ed input e le. An appropriate interpretation and manipulation of such input ‐output e les yields the estimates of the parameters. Flight data for lateral ‐directional dynamics are analyzed for various combinations and types of control inputs, and suitable control input forms are identie ed for better estimation via the proposed method. Robustness of the method with respect to measurement noise is demonstrated by its applicability to simulated e ight data with pseudomeasurement noise, and to real-e ight data.

77 citations


"Estimation of nonlinear airship par..." refers methods in this paper

  • ...Parameter estimation for aerial vehicles is carried out using conventional methods such as Least squares, Maximum Likelihood Estimation [1], [2] and unconventional methods such as Artificial Neural Network (ANN) [3], [4], Neural Gauss-Newton [5], [6] and Recurrent Neural Network [7]....

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