Abstract
This paper presents a flight control system for an organic flight array (OFA) with a new configuration consisting of multimodularized ducted-fan unmanned aerial vehicles. The OFA has a distinguished advantage of assembling or separating with respect to its missions or operational conditions because of its reconfigurable structure. Therefore, designing a controller that can be flexibly applied in each situation is necessary. First, a dynamic modeling of the OFA based on a single ducted-fan vehicle is performed. Second, the inner loop for attitude control is designed through dynamic model inversion and a PD controller. However, an adaptive control component is needed to flexibly cope with the uncertainty because the operating environment of the OFA is varied, and uncertainty exists depending on the number of modules to be assembled and disturbances. In addition, the performance of the neural network adaptive controller is verified through a numerical simulation according to two scenarios.
Highlights
Unmanned aerial vehicles (UAVs) with many types, sizes, and ways of flight have been used in various fields during the past decades
The model uncertainty that can occur in accordance with the operational concept of the organic flight array composed of ducted-fan vehicles is defined as the model inversion error of the process of DMI, and it is significant to verify whether such uncertainty is mitigated through the radial basis function neural network controller which enables control
This study presented a concept of the organic flight array that can simultaneously perform various missions with a single ducted-fan vehicle through the assembling, separation, and cooperation modes
Summary
Unmanned aerial vehicles (UAVs) with many types, sizes, and ways of flight have been used in various fields during the past decades. A similar concept with the DFA was used to overcome various disadvantages by assembling a ducted-fan vehicle. The model uncertainty that can occur in accordance with the operational concept of the organic flight array composed of ducted-fan vehicles is defined as the model inversion error of the process of DMI, and it is significant to verify whether such uncertainty is mitigated through the radial basis function neural network controller which enables control. This study proposed an adaptive control method to cope with the disadvantages and limitations of other studies on flight array by using the radial basis function neural network that can consider the nonlinear characteristics and model uncertainties. The dynamic model of the OFA in this process was defined based on the single ducted-fan vehicle [4].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
