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Article

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

1
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
2
Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
*
Author to whom correspondence should be addressed.
Robotics 2018, 7(4), 66; https://doi.org/10.3390/robotics7040066
Received: 1 September 2018 / Revised: 19 October 2018 / Accepted: 20 October 2018 / Published: 23 October 2018
(This article belongs to the Special Issue Feature Papers)
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. View Full-Text
Keywords: model-free control; flexible wing aircraft; reinforcement learning; optimal control model-free control; flexible wing aircraft; reinforcement learning; optimal control
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MDPI and ACS Style

Abouheaf, M.; Gueaieb, W.; Lewis, F. Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft. Robotics 2018, 7, 66. https://doi.org/10.3390/robotics7040066

AMA Style

Abouheaf M, Gueaieb W, Lewis F. Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft. Robotics. 2018; 7(4):66. https://doi.org/10.3390/robotics7040066

Chicago/Turabian Style

Abouheaf, Mohammed, Wail Gueaieb, and Frank Lewis. 2018. "Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft" Robotics 7, no. 4: 66. https://doi.org/10.3390/robotics7040066

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