Use of Neural Networks in Identification and Control of on-Board Dynamic Systems

Abstract

The article presents the principles of creating a predictive control strategy based on a neural network model of aviation systems. The use of neural networks for identifying and controlling dynamic systems is substantiated, including the use of model predictive control for predicting the state (parameters) of an aircraft as a dynamic system, more suitable among neural network architectures. For model predictive control, a model of an object is used to predict the future behavior of the object, and an optimization algorithm is used to select a control input that optimizes the future operation of the object. To reflect the dynamics of the system in real time, the issues of training a neural network were considered, for which a standard model, the NARMA model, was chosen as the structure of the model.