Motion Control of Supersonic Passenger Aircraft Using Machine Learning Methods

Abstract

Motion control of modern and advanced aircraft has to be provided under conditions of incomplete and inaccurate knowledge of their parameters and characteristics, possible flight regimes, and environmental influences. In addition, a variety of abnormal situations may arise during flight, in particular, equipment failures and structural damage. The control system must be able to adapt to these changes by adjusting the control laws in use. The tools of the adaptive control allows us to meet this requirement. One of the effective approaches to the implementation of adaptivity concepts is the approach based on methods and tools of neural network modeling and control. In this case, a fairly common option in solving such problems is the use of recurrent neural networks, in particular, networks of NARX and NARMAX type. However, in a number of cases, in particular for control objects with complicated dynamic properties, this approach is ineffective. As a possible alternative, it is proposed to consider deep neural networks used both for modeling of dynamical systems and for their control. The capabilities of this approach are demonstrated on the example of a real applied problem, in which the control law of longitudinal angular motion of a supersonic passenger airplane is synthesized. The results obtained allow us to evaluate the effectiveness of the proposed approach, including the case of failure situations.