Journal Article

Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft

Gueaieb, Wail, Lewis, Frank, Abouheaf, Mohammed

2018

Overview

Classical gradient-based approximate dynamic programming approaches provide reliable and fast solution platforms for various optimal control problems. However, their dependence on accurate modeling approaches poses a major concern, where the efficiency of the proposed solutions are severely degraded in the case of uncertain dynamical environments. Herein, a novel online adaptive learning framework is introduced to solve action-dependent dual heuristic dynamic programming problems. The approach does not depend on the dynamical models of the considered systems. Instead, it employs optimization principles to produce model-free control strategies. A policy iteration process is employed to solve the underlying Hamilton–Jacobi–Bellman equation using means of adaptive critics, where a layer of separate actor-critic neural networks is employed along with gradient descent adaptation rules. A Riccati development is introduced and shown to be equivalent to solving the underlying Hamilton–Jacobi–Bellman equation. The proposed approach is applied on the challenging weight shift control problem of a flexible wing aircraft. The continuous nonlinear deformation in the aircraft’s flexible wing leads to various aerodynamic variations at different trim speeds, which makes its auto-pilot control a complicated task. Series of numerical simulations were carried out to demonstrate the effectiveness of the suggested strategy.

• Publisher: MDPI AG
• Subject: Adaptive learning / Aerodynamics / Aircraft / Automatic pilots / Control theory / Critics / Dynamic models / Dynamic programming / flexible wing aircraft / Flexible wings / Hang gliding / Heuristic / Iterative methods / Kinematics / Learning / model-free control / Neural networks / Optimal control / Optimization / reinforcement learning / Unmanned aircraft