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![Figure 1. System identification diagram [1].](profile/Fahimeh-Barzamini/publication/352234649/figure/fig1/AS:1050423800651779@1627451958821/System-identification-diagram-1_Q320.jpg)
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... it can be said that, aircraft system identification is applied to model which is functionally dependent to the aerodynamic forces and moments on aircraft motion and control variables [1]. In accordance with [1], the general approach to aircraft system identification includes essential steps which are shown in Figure 1. In recent years, much research has been conducted aiming at estimating the aircraft system identification [2]. ...
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... multilayer perceptron structure MLP with two hidden layers and three neurons per layer is employed for the ANN Model. This architecture is shown in Figure 14. The Levenberg-Marquardt algorithm (LMA/LM) has been employed in the networks training process. ...
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... to demonstrate the accuracy and precision of the trained network, the all training, evaluation, testing errors for both longitudinal and lateraldirectional modes are presented in Tables 8 and 9 respectively to ensure the precision of adapting the ANN model to the original model. In order to evaluation and comparison, the output aerodynamic forces and moments from the simulation and the ANN Model which is obtained by pre-trained neural networks are presented in Figure 15. As can be observed, the outputs resulted from both the simulation model (the original model) and the ANN model are well adapted on each other. ...
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... can be observed, the outputs resulted from both the simulation model (the original model) and the ANN model are well adapted on each other. The measured error from the difference between these models outputs is indicated in Figure 16. Given the value and rank of the error, it can be understood that network training was performed well. ...
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... is, indeed, a neural network that realizes the Sugeno fuzzy system using the network. The basic architecture of the ANFIS for two inputs is represented in Figure 17. The membership function is adjusted in an adaptive form using neural networks and system inputs and outputs data. ...
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... identification using an adaptive neural fuzzy inference system is similar to system identification using a neural network based on an alternative model instead of the aerodynamic model in simulations. In order to examine the accuracy of ANFIS training, the error distribution curve for each of the aerodynamic parameters is indicated in Figure 18. Considering the mean error and error deviation of each of the aerodynamic parameters observed in the Figure 18, it can be understood that the training process is carried out well. ...
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... order to examine the accuracy of ANFIS training, the error distribution curve for each of the aerodynamic parameters is indicated in Figure 18. Considering the mean error and error deviation of each of the aerodynamic parameters observed in the Figure 18, it can be understood that the training process is carried out well. ...
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... measured outputs including the aerodynamic force and moment coefficients are given in Figure 19 to evaluate and compare the results with the original model. Besides, the difference between the measured outputs value from the original model and the surrogated model are shown in Figure 20. ...
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... order to compare the performance and accuracy of the Intelligent Models, the error charts of the ANN and ANFIS models are compared. Such a comparison is illustrated in Figure 21. According to it, the ANFIS has less error in í µí° ¶ ،í µí° ¶ ،í µí° ¶ , relatively equal error in í µí° ¶ ، í µí° ¶ and more error in í µí° ¶ than the neural networks. ...
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... better accuracy in training and generating a more accurate model, it has been tried that the training process to be done separately for each of the aerodynamic forces and moments. The accuracy of the neural networks model can be examined using the training error represented in Tables 8 and 9. Furthermore, the accuracy of ANFIS can be observed taking into account the Root Mean Square Error (RMSE) of each of the parameters given in Figure 18. To ensure the accuracy of the models gained from neural network and ANFIS, it can be seen in Figures 15 and 19, respectively, that the outputs obtained from the simulation (the original model), as well as the outputs obtained from the alternative models for similar inputs, match to each other, and the difference between these two outputs can be observed in Figures 16 and 20, respectively, representing a low value of the difference between outputs (error value).To evaluate the accuracy of each of the alternative models obtained by neural network and ANFIS, the output error values of each of the models, for each of the aerodynamic force and moment coefficients, are presented in Figure 21. ...
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... accuracy of the neural networks model can be examined using the training error represented in Tables 8 and 9. Furthermore, the accuracy of ANFIS can be observed taking into account the Root Mean Square Error (RMSE) of each of the parameters given in Figure 18. To ensure the accuracy of the models gained from neural network and ANFIS, it can be seen in Figures 15 and 19, respectively, that the outputs obtained from the simulation (the original model), as well as the outputs obtained from the alternative models for similar inputs, match to each other, and the difference between these two outputs can be observed in Figures 16 and 20, respectively, representing a low value of the difference between outputs (error value).To evaluate the accuracy of each of the alternative models obtained by neural network and ANFIS, the output error values of each of the models, for each of the aerodynamic force and moment coefficients, are presented in Figure 21. However, in this paper, the identification method was done based on PRBS input, and if real inputs were available from aircraft flight tests, the results, as well as ANN and ANFIS training structure according to the real measured inputs and outputs, would be different. ...
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... accuracy of the neural networks model can be examined using the training error represented in Tables 8 and 9. Furthermore, the accuracy of ANFIS can be observed taking into account the Root Mean Square Error (RMSE) of each of the parameters given in Figure 18. To ensure the accuracy of the models gained from neural network and ANFIS, it can be seen in Figures 15 and 19, respectively, that the outputs obtained from the simulation (the original model), as well as the outputs obtained from the alternative models for similar inputs, match to each other, and the difference between these two outputs can be observed in Figures 16 and 20, respectively, representing a low value of the difference between outputs (error value).To evaluate the accuracy of each of the alternative models obtained by neural network and ANFIS, the output error values of each of the models, for each of the aerodynamic force and moment coefficients, are presented in Figure 21. However, in this paper, the identification method was done based on PRBS input, and if real inputs were available from aircraft flight tests, the results, as well as ANN and ANFIS training structure according to the real measured inputs and outputs, would be different. ...
Context 13
... accuracy of the neural networks model can be examined using the training error represented in Tables 8 and 9. Furthermore, the accuracy of ANFIS can be observed taking into account the Root Mean Square Error (RMSE) of each of the parameters given in Figure 18. To ensure the accuracy of the models gained from neural network and ANFIS, it can be seen in Figures 15 and 19, respectively, that the outputs obtained from the simulation (the original model), as well as the outputs obtained from the alternative models for similar inputs, match to each other, and the difference between these two outputs can be observed in Figures 16 and 20, respectively, representing a low value of the difference between outputs (error value).To evaluate the accuracy of each of the alternative models obtained by neural network and ANFIS, the output error values of each of the models, for each of the aerodynamic force and moment coefficients, are presented in Figure 21. However, in this paper, the identification method was done based on PRBS input, and if real inputs were available from aircraft flight tests, the results, as well as ANN and ANFIS training structure according to the real measured inputs and outputs, would be different. ...
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Citations
... In such cases, the system identification method, which is an experimental method, is often preferred (Wei et al. 2017;Ivler et al. 2021;Simmons 2021). There are many examples in the literature of the use of the system identification method in creating models of difficult and complex systems such as aircrafts, helicopters and quadcopters (Geluardi et al. 2018;Yu et al. 2020;Ebrahimi and Barzamini 2021). ...
In this study, an experimental set of a Single-DoF Copter system is created and transfer functions that could model the dynamics of the physical system with high accuracy were investigated. In order to model the dynamics of the physical system with the highest accuracy, the five different transfer functions have been proposed, in which the zero and pole values are determined by optimizing with the Vibrating Particle System Algorithm. Integral Square Error (ISE), Integral Time Square Error (ITSE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) functions, which are widely used in the literature in determining transfer functions, are determined as fitness functions. In order to verify the transfer functions, the responses of the transfer functions and the experimental system response are presented comparatively, and their suitability was evaluated. It has been observed that the proposed method is successful in defining the transfer function of the experimental system, and the compatibility of the obtained transfer functions with the system response is between 75.407% and 98.612% accuracy.
