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

Mazhar, Farrukh; Choudhry, M.A.; Shehryar, Muhammad

doi:10.1177/0954410018783131

Cited by 8 publications

(5 citation statements)

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“…Fig 3 shows the block diagram of the proposed algorithm to estimate the required parameters. The airship simulator uses the nonlinear dynamic equations of the airship model for UETT airship [39,40]. Mass matrix parameters for the airship are given in S1 Appendix.…”

Section: Resultsmentioning

confidence: 99%

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

et al. 2021

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An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

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Section: Resultsmentioning

confidence: 99%

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

et al. 2021

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“…The radial basis function network is a network with a simple architecture since uses radial basis functions as an activation function (Mazhar et al, 2019). Besides, one important characteristic of RBFNNs is the presence of linear-in-the-parameters weighting coefficients resulting in faster learning algorithms (Ayala, 2016).…”

Section: Vehicle Dynamic Model and Magic Formulamentioning

confidence: 99%

Nonlinear Tire Model Approximation Using Artificial Neural Networks

Sousa

Ayala

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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This paper presents a comparative study between two different approximation approaches for the traditional Magic Formula tire model based on Artificial Neural Network (ANN), in specific, Radial Basis Function Network and Multilayer Perceptron Network, with the view to approximate longitudinal and lateral friction coefficients curves. Simulation results are considered satisfactory, showing that, the MLP network presented better results compared to the RBF network which indicates that the prediction of the friction coefficients curves is driven close to the reference derived from the Magic Formula. A nonlinear physical-mathematical model is used as the real plant for the comparison between the MLP tire model and the Magic Formula, considering torque and different steering angles as input. Moreover, the results show that the implemented network is adequate for applications in simulated ground vehicles.

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“…There are a lot of grids in a BIW finite element model so that the calculation cost may be high while the optimization efficiency may be low. Optimization methods based on surrogate models, such as the response surface method, 8,9 radial basis function (RBF), 10,11 kriging method, 12,13 support vector machine, 14,15 and artificial neural network method, 16,17 are relatively more efficient. On the other hand, it remains challenging to determine the most suitable surrogate modeling method for approximation and optimization of the responses.…”

Section: Introductionmentioning

confidence: 99%

Structural optimization design of BIW using NSGA-III and entropy weighted TOPSIS methods

Chen,

Lu,

Feng

et al. 2023

Advances in Mechanical Engineering

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Multi-objective optimization is always a difficult and hot problem for the static and dynamic performance and lightweight optimization design of a body-in-white (BIW). In this regard, a hybrid method coupled with NSGA-III and entropy weighted TOPSIS methods was put forward here. Effects of each design variable on the BIW performance was studied by means of the contribution method so as to optimize various design variables. Further, an experimental design was carried out by means of a radial basis function model to approximate each performance index of the automotive body. On the base of that, a comprehensive method where the Pareto frontier is searched by means of NSGA-III while the optimal solution is selected by means of the entropy weighted TOPSIS method was proposed based on lightweight, static and dynamic performances to analyze BIW performances to determine the contradictory optimal solution among various BIW factors. Afterward, our numerical verifications demonstrate that the BIW performances are improved significantly so that it can be feasible to optimize static and dynamic BIW performances and the lightweight performance of the BIW can be guaranteed accordingly.

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

Cited by 8 publications

References 50 publications

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

Nonlinear Tire Model Approximation Using Artificial Neural Networks

Structural optimization design of BIW using NSGA-III and entropy weighted TOPSIS methods

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