Abstract: In this paper, it is aimed to improve autonomous flight
performance of a load-carrying (payload: 3 kg and total: 6kg)
unmanned aerial vehicle (UAV) through active wing and horizontal
tail active morphing and also integrated autopilot system parameters
(i.e. P, I, D gains) and UAV parameters (i.e. extension ratios of wing
and horizontal tail during flight) design. For this purpose, a loadcarrying
UAV (i.e. ZANKA-II) is manufactured in Erciyes
University, College of Aviation, Model Aircraft Laboratory is
benefited. Optimum values of UAV parameters and autopilot
parameters are obtained using a stochastic optimization method.
Using this approach autonomous flight performance of UAV is
substantially improved and also in some adverse weather conditions
an opportunity for safe flight is satisfied. Active morphing and
integrated design approach gives confidence, high performance and
easy-utility request of UAV users.
Abstract: Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.