Elements of the Smart Geometrical Control Technology for Unmanned Aerial Vehicles

The authors propose a multimodal control system for an unmanned quadcopter integrating various information input, processing, and transmission methods, as well as the methods of geometrical and smart control. The smart control component is associated with the processing of voice and gesture commands recognized using artificial neural networks of deep learning. The geometrical components are associated with routing, solving problems of trajectory and target following, as well as video camera aiming. It is also a minimum-time problem. The authors provide a generic design of the control system. Controlling the on-board camera plays a significant role. The video camera monitors a ground-based (moving or stationary) object, and it can switch to the point determined by the operator. To forecast the movement of the target, the authors use an artificial neural network (ANN) with a new s-parabola activation function that facilitates fast learning and calculation of the set function. To model the control during the mission, the authors built and analyzed a dynamic control model as an automatic control system based on transfer functions. During the flight, the system experiences wind loads that hinder precise control and camera aiming for ground-based targets. The system is modeled in MATLAB and Simulink software.