Design of a Neural Network Controller for Flying Robots Using a PIP and a Backstopping Trainer

In this paper, an intelligent neural network controller is designed that can balance a quadrotor. After comparing the two types of independent reverse controllers and PID in the simulation environment, their differences are stored as data in the software. By specifying the input data of the controller and the target data and using the feedforward neural network architecture and a Narx architecture, intelligent controllers are designed and the obtained results are shown in several stability detection diagrams. The obtained results show that the equilibrium and vertical flight control are quite acceptable. Finally, the results are tested in a practical model on a real vertical plane. In the practical model, due to the limited amount of training data, the performance of Narx network is much more appropriate than the performance of the feed network. In fact, the complexity of the Narx recursion algorithm helps to interpret the complex dynamic behavior of the system well despite the low data. The disadvantage of this type of network is its very complex hardware implementation, which requires a much more powerful processor and memory to run on the controllers.