Combined Use of Dynamic Inversion and Reinforcement Learning for Motion Control of an Supersonic Transport Aircraft

The task of aircraft motion control has to be solved under conditions of numerous heterogeneous uncertainties both in the aircraft motion model and in the environment in which the aircraft is flying. These uncertainties, in particular, are caused by the fact that in the flight of the aircraft can occur various kinds of abnormal situations caused by failures of equipment and systems of the aircraft, damage to the airframe and propulsion system of the aircraft. Some of these failures and damages have a direct impact on the dynamic characteristics of the aircraft as a control object. In this regard, the problem arises of such an adjustment of aircraft control algorithms that would provide the ability to adapt to the changed dynamics of the aircraft. It is extremely difficult, and in some cases impossible, to foresee in advance all possible damages, failures and their combinations. Hence, it is necessary to implement adaptive flight control algorithms that are able to adjust to the changing situation. One of the effective tools for solving such problems is reinforcement learning in the Approximate Dynamic Programming (ADP) variant, in combination with artificial neural networks. In the last decade, a family of methods known as Adaptive Critic Design (ACD) has been actively developed within the ADP approach to control the behavior of complex dynamic systems. In our paper we consider the application of one of the variants of the ACD approach, namely SNAC (Single Network Adaptive Critic) and its development through its joint use with the method of dynamic inversion. The effectiveness of this approach is demonstrated on the example of longitudinal motion control of a supersonic transport airplane.