Mujahed Al-Dhaifallah

Quadrotors, commonly known as drones or unmanned aerial vehicles (UAVs), play an important role in load transportation. The complex vehicles used for transporting loads and surveillance purposes can be replaced through the ease of use and mechanical simplicity of the quadrotors. This study aims to introduce a new way of controlling the slung load system of the quadrotor after applying the fractional-order sliding mode control (FOSMC) method for performance enhancement. This method in the presence of external disturbances is likely to help stabilize and track the quadrotor with minimized swinging of the attached load. The results of this study prove the robustness of FOSMC through analysis, outcomes, and graphical representations that show the effect with various angles for a clear and conceptual understanding. The present study contributes to the literature by designing a robust FOSMC for a quadrotor with the help of external disturbances. The results of this study could be applied to the development of the design of upcoming drones which can increase the efficiency as well as the accuracy of load transportation. Further applications in the fields of rescue, agriculture, construction, research, and advancement of the fraction calculus-based control method are recommended using the FOSMC method.

Microbial fuel cells (MFCs) are biocells that use microorganisms as biocatalysts to break down organic matter and convert chemical energy into electrical energy. Presently, the application of MFCs as alternative energy sources is limited by their low power attribute. Optimization of MFCs is very important to harness optimum energy. In this study, we develop optimal data-driven models for a typical MFC synthesized from polymethylmethacrylate and two graphite plates using machine learning algorithms including support vector regression (SVR), artificial neural networks (ANNs), Gaussian process regression (GPR), and ensemble learners. Power density and output voltage were modeled from two different datasets; the first dataset has current density and anolyte concentration as features, while the second dataset considers current density and chemical oxygen demand as features. Hyperparameter optimization was carried out on each of the considered machine learning-based models using Bayesian optimization, grid search, and random search to arrive at the best possible models for the MFC. A model was derived for power density and output voltage having 99% accuracy on testing set evaluations.