Archive | 2019
Neural Network Black Box Modeling of Nonlinear Dynamical Systems: Aircraft Controlled Motion
Abstract
Abstract Chapter 4 deals with black box neural network modeling of nonlinear dynamical systems. As an example, we discuss aircraft controlled motion. First of all, we consider the design process for ANN empirical dynamical system models, which belong to the family of black box models. The basic types of such models are described, and approaches to taking into account perturbations acting on the dynamical system are analyzed. Then, we construct an ANN model of the aircraft motion based on a multilayer neural network. As a baseline model, a multilayered neural network with feedbacks and delay lines is considered, in particular, NARX and NARMAX-type models. The training of such an ANN model in batch mode and in real-time mode is described. Next, the performance of the obtained ANN model of the aircraft motion is evaluated for an example problem of the longitudinal short-period aircraft motion modeling. The performance evaluation of the model is carried out using computational experiments. One of the most important applications of dynamical models is related to the problem of adaptive control for such systems. We consider the solution to the problem of adaptive fault-tolerant control for nonlinear dynamical systems operating under uncertainty conditions to demonstrate the potential capabilities of ANN models in this area. We apply both the model reference adaptive control (MRAC) and model predictive control (MPC) methods using empirical (black box)-type ANN models. Also, synthesis of neurocontrollers is carried out.