scholarly journals Design of Low Altitude Long Endurance Solar-Powered UAV Using Genetic Algorithm

Aerospace ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 228
Author(s):  
Abu Bakar ◽  
Li Ke ◽  
Haobo Liu ◽  
Ziqi Xu ◽  
Dongsheng Wen
Keyword(s):  
Genetic Algorithm ◽  
Static Stability ◽  
Estimation Model ◽  
Nonlinear Simulation ◽  
Low Altitude ◽  
Solar Powered ◽  
Materials Used ◽  
Six Degree Of Freedom ◽  
Long Endurance ◽  
Wing Airfoil

This paper presents a novel framework for the design of a low altitude long endurance solar-powered UAV for multiple-day flight. The genetic algorithm is used to optimize wing airfoil using CST parameterization, along with wing, horizontal and vertical tail geometry. The mass estimation model presented in this paper is based on structural layout, design and available materials used in the fabrication of similar UAVs. This model also caters for additional weight due to the change in wing airfoil. The configuration is optimized for a user-defined static margin, thereby incorporating static stability in the optimization. Longitudinal and lateral control systems are developed for the optimized configuration using the inner–outer loop strategy with an LQR and PID controller, respectively. A six degree-of-freedom nonlinear simulation is performed for the validation of the proposed control scheme. The results of nonlinear simulations are in good agreement with static analysis, validating the complete design process.

Design of a Solar Powered, Unmanned, Low Altitude, High Endurance Airship

2015 ◽  
Vol 798 ◽  
pp. 556-564 ◽  
Author(s):  
Onur Sinan Sonmez ◽  
H. Nafiz Alemdaroglu
Keyword(s):  
Design Parameters ◽  
Design Criteria ◽  
Detailed Design ◽  
General Design ◽  
Design Specifications ◽  
Unmanned Air Vehicle ◽  
Air Vehicle ◽  
Low Altitude ◽  
Solar Powered ◽  
Long Endurance

This article presents the design of a low altitude long endurance solar powered unmanned airship. In the first part of the article, a brief introduction about the airships, their potential areas of usage, the advantages of using an unmanned air vehicle, and a comparison between unmanned counterparts will be given. In the second part, the design criteria and the general design specifications will be outlined briefly. During the design, emphasis is given on the general design parameters of the Lighter Than Air (LTA) vehicles. Lastly, the detailed design results, in order to reach the projected design parameters, have been presented.

Withdrawal: Low-Altitude Autonomous Flight of a Solar-Powered UAV System

2020 ◽  
Author(s):  
Ying Bi ◽  
Liyang Zhou ◽  
Yang Wen ◽  
Xiaoping Ma ◽  
Yong He
Keyword(s):  
Autonomous Flight ◽  
Low Altitude ◽  
Solar Powered

Optimization of Preloaded Coefficient Based on the Adapted CBR

2009 ◽  
Vol 628-629 ◽  
pp. 13-18
Author(s):  
H.L. Li ◽  
Li Hui Lang ◽  
W. Jiao ◽  
H.Z. Su
Keyword(s):  
Genetic Algorithm ◽  
Optimum Design ◽  
Prediction Error ◽  
Selection Model ◽  
Knowledge Based Systems ◽  
Estimation Model ◽  
Knowledge Based ◽  
Adjustment Model ◽  
Successful Technique

Selecting an appropriate preloaded coefficient has always been a challenge in wire- winding prestressed structure optimum design. Cased-based reasoning (CBR) has become a successful technique for knowledge-based systems in many domains. However, hardly any research has addressed the issue of how to generate the adaptation solution when the case has been retrieved. The present paper investigates the adoption of genetic algorithm(GA) to explore the suitable adjustment model. Two adapted model were presented and assessed in terms of their mean relative prediction error rates.The experiment results shown that applying GA to adjust the preloaded coefficient selection model is a feasible approach to largely improve the accuracy of estimation model. It also demonstrate that the adapted CBR presents better estimate accuracy than the results ontained by other unadapted CBR methods.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

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