Online Model‐Free Reinforcement Learning for the Automatic Control of a Flexible Wing Aircraft

FieldValue
dc.contributor.authorAbouheaf, Mohammed
dc.contributor.authorGueaieb, Wail
dc.contributor.authorLewis, Frank
dc.date.accessioned2021-03-31T18:38:39Z
dc.date.available2021-03-31T18:38:39Z
dc.date.issued2020
dc.identifier.issn1751-8652
dc.identifier.urihttp://hdl.handle.net/10393/41947
dc.description.abstractThe control problem of the flexible wing aircraft is challenging due to the prevailing and high nonlinear deformations in the flexible wing system. This urged for new control mechanisms that are robust to the real-time variations in the wing's aerodynamics. An online control mechanism based on a value iteration reinforcement learning process is developed for flexible wing aerial structures. It employs a model-free control policy framework and a guaranteed convergent adaptive learning architecture to solve the system's Bellman optimality equation. A Riccati equation is derived and shown to be equivalent to solving the underlying Bellman equation. The online reinforcement learning solution is implemented using means of an adaptive-critic mechanism. The controller is proven to be asymptotically stable in the Lyapunov sense. It is assessed through computer simulations and its superior performance is demonstrated on two scenarios under different operating conditions.
dc.description.sponsorshipThis work was partially funded by Ontario Center of Excellence (OCE) (Funding Reference Number:27404)
dc.language.isoen
dc.subjectOptimal Control
dc.subjectReinforcement Learning
dc.subjectValue Iteration
dc.subjectAdaptive Critics
dc.subjectAircraft Control
dc.subjectDynamic Programming
dc.titleOnline Model‐Free Reinforcement Learning for the Automatic Control of a Flexible Wing Aircraft
dc.typeArticle
dc.identifier.doi10.1049/iet-cta.2018.6163
CollectionScience informatique et génie électrique - Publications // Electrical Engineering and Computer Science - Publications

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