Guidance Mechanism for Flexible-Wing Aircraft Using Measurement-Interfaced Machine-Learning Platform

The autonomous operation of flexible-wing aircraft poses theoretical and technical challenges not yet addressed in the literature. The lack of an exact modeling framework is due to the complex nonlinear aerodynamics driven by the deformations of the flexible-wings, which in turn complicates the controls and instrumentation setup of the navigation system. This urges for innovative approaches to interface affordable instrumentation platforms to autonomously control this type of aircraft. This article leverages the 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 based on two improved and interacting model-free control strategies in real time. The first strategy is concerned with achieving the tracking objectives, whereas 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.