Intelligent Quadcopter Control Using Artificial Neural Networks

Abstract

An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high dimensions. About ten percent improvement over state-of-the-art controllers is observed and results are reported both numerically and graphically. Promising results encourage to use the type of controller proposed for various real applications.