# Balance Control of Brushless Direct Current Motor Driven Two-Rotor UAV
## 6 Conclusions  
Since the control and structure of multi-rotor systems in UAVs are much more complex than those of two-rotor systems, two-rotor systems are widely used in the literature to develop controllers. In this study, the stability control of a 2R-UAV with BLDCM as the drive element was performed. Differing from the studies in the literature, the speeds of the BLDCMs as well as the balance angle were controlled in a closed loop, improving the control performance of the balance angle. The controllers used were the PID controller, a classical control method; the 2-DOF PID controller, an advanced control method; and the AF 2-DOF PID controller, which combines Fuzzy Logic, an artificial intelligence-based control method, with the 2-DOF PID controller. To examine the real-time performance of the proposed controller structure, a Co-Simulation structure was created using an STM32F429I microprocessor and Matlab/Simulink simulations, and the results were examined.  
Co-simulation results for the classical PID, advanced 2-DOF PID, and suggested AF 2-DOF PID controllers used for the control of the 2R-UAV Balance System are presented separately for fixed and variable reference balance angles, and loaded conditions. Examining these results, the reference balance angle was achieved in a short time in all operating situations and continued to operate stably. Nonetheless, it is evident that, particularly with changeable reference values and under load, the AF 2-DOF PID controller suggested in this work, thanks to its adaptive characteristics, performs better than both the PID and 2-DOF PID controllers. As seen in Table 8, the AF 2-DOF PID has 0% overshoot and the  
Figure 25: Changes on motor speeds when condition of adding load.  
minimum steady state error in all scenarios. Also, as seen in Figure 24, when the load was added, although there were no significant changes in the AF 2-DOF PID controlled system, a 0.1-degree drop occurred in the classical PID controlled system and a 0.15-degree drop in the 2-DOF PID controlled system.  
The BLDCM driven 2R-UAV balance system modeled and controlled in this study is open to development in terms of its structure and suitable for the application of different control algorithms. In further studies, Artificial Neural Networks (ANN) and Deep Learning-based controller structures, which are artificial intelligence methods, will be tried for the control of the 2R-UAV Balance System. In addition, in this study, the AF 2-DOF PID controller structure, which is recommended for the control of 2R-UAV and shows successful performance, will be used to increase the control performance of the quad-rotor UAV, which is widely used today.  
Conceptualization, T.Y., H.C. and I.C.; methodology, H.C. and I.C.; software, H.C. and I.C.; validation, T.Y., H.C. and I.C.; formal analysis, I.C.; investigation, I.C.; resources, I.C.; data curation, I.C.; writing--original draft preparation, T.Y., H.C. and I.C.; writing--review and editing, T.Y. and H.C.; visualization, T.Y.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. and I.C. All authors have read and agreed to the published version of the manuscript.  
This work has been supported within project MF21.75 by Firat University Scientific Research Projects Unit.  
The raw data supporting the conclusions of this article will be made available by the Author on request.  
The Author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
The following abbreviations are used in this manuscript:  
BLDCM Brushless Direct Current Motor  
DOF Degree of Freedom  
PID Proportional Integral Derivative Controller  
UAV Unmanned Areal Vehicle  
rpm Revolutions per Minute