An indirect adaptive neural control of a visual-based quadrotor robot for pursuing a moving target

- Using perspective image moments for the implementation of the IBVS.
- Designing an indirect adaptive neural controller for an under-actuated quadrotor robot.
- Using an RBF neural network in designing an image-based controller for tracking a moving target.
- The online training of a neural network for compensating uncertainties in the quadrotor and in image dynamics.

This paper aims to use a visual-based control mechanism to control a quadrotor type aerial robot which is in pursuit of a moving target. The nonlinear nature of a quadrotor, on the one hand, and the difficulty of obtaining an exact model for it, on the other hand, constitute two serious challenges in designing a controller for this UAV. A potential solution for such problems is the use of intelligent control methods such as those that rely on artificial neural networks and other similar approaches. In addition to the two mentioned problems, another problem that emerges due to the moving nature of a target is the uncertainty that exists in the target image. By employing an artificial neural network with a Radial Basis Function (RBF) an indirect adaptive neural controller has been designed for a quadrotor robot in search of a moving target. The results of the simulation for different paths show that the quadrotor has efficiently tracked the moving target.