Combined use of dynamic inversion and reinforcement learning for optimal adaptive control of supersonic transport airplane motion

We consider the problem of aircraft motion control under uncertainties caused by incomplete and inaccurate knowledge of the aircraft characteristics, as well as by abnormal situations in flight that affect the properties of the aircraft as a control object. One of the effective tools for solving problems of this kind, providing the adjustment of aircraft control algorithms taking into account its changed dynamics, is reinforcement learning in the variant of Approximate Dynamic Programming (ADP), 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 dynamical systems. The paper discusses the application of one variant of the ACD approach, namely SNAC (Single Network Adaptive Critic) and its development through combined use with the dynamic inversion method. This approach makes it possible to form an optimal adaptive control law for aircraft motion. Its effectiveness is demonstrated on the example of longitudinal motion control for supersonic transport airplane (SST).