Synthesis of neurocontroller for multirotor unmanned aerial vehicle based on neuroemulator

This paper presents a method of creating a neurocontroller based on a multilayer perceptron for an unmanned aerial vehicle. We show how a neural network can effectively emulate dynamic characteristics of an aerial craft. Another network learns to control the emulator, using backpropagation algorithm to calculate the error in its control signal. A set of parameters is used to analyze the efficiency of the stabilization and the weights of the neurocontroller are adjusted accordingly. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described method can be used to remotely control unmanned aerial vehicles operating in changing environment.