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

TL;DR

This paper introduces a novel machine learning-based guidance system for autonomous flexible-wing aircraft, using real-time measurements and adaptive control strategies to address complex nonlinear aerodynamics.

Contribution

It develops a measurement-interfaced, model-free control framework employing online value iteration and neural networks for flexible-wing aircraft navigation.

Findings

Experimental validation confirms system stability.

Real-time control strategies improve navigation accuracy.

Adaptive critics effectively approximate control assessments.

Abstract

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.