Semi-empirical Neural Network Based Modeling and Identification of Controlled Dynamical Systems

One of the critical elements of the process of creating new engineering systems is the formation of mathematical and computer models that provide solutions to the problems of creating and using such systems. For such systems, typical is a high level of complexity of the objects and processes being modeled, their multi-dimensionality, non-linearity and non-stationarity, the diversity and complexity of the functions implemented by the simulated object. The solution to the problems of modeling for objects of this kind is significantly complicated by the fact that the corresponding models have to be formed in the presence of multiple and diverse uncertainties, such as incomplete and inaccurate knowledge of the characteristics and properties of the object being modeled, as well as the conditions in which the object will operate. Besides, during operation, the properties of the object being modeled may change, including sharp and significant, for example, due to equipment failures and/or structural damages. An approach to the formation of gray box models (semi-empirical models) for systems of this kind, based on combining theoretical knowledge about the object of modeling with the methods and tools of neural network modeling, is considered. As an example, we demonstrate the formation of a model for the longitudinal angular motion of a maneuverable aircraft, as well as the identification of the aerodynamic characteristics for the aircraft included in this model.