  The autonomous operation of flexible-wing aircraft is technically challenging
and has never been presented within literature. The lack of an exact modeling
framework is due to the complex nonlinear aerodynamic relationships governed by
the deformations in the flexible-wing shape, which in turn complicates the
controls and instrumentation setup of the navigation system. This urged for
innovative approaches to interface affordable instrumentation platforms to
autonomously control this type of aircraft. This work leverages ideas from
instrumentation and measurements, machine learning, and optimization fields in
order to develop an autonomous navigation system for a flexible-wing aircraft.
A novel machine learning process based on a guiding search mechanism is
developed to interface real-time measurements of wing-orientation dynamics into
control decisions. This process is realized using an online value iteration
algorithm that decides on two improved and interacting model-free control
strategies in real-time. The first strategy is concerned with achieving the
tracking objectives while the second supports the stability of the system. A
neural network platform that employs adaptive critics is utilized to
approximate the control strategies while approximating the assessments of their
values. An experimental actuation system is utilized to test the validity of
the proposed platform. The experimental results are shown to be aligned with
the stability features of the proposed model-free adaptive learning approach.
