High fidelity progressive reinforcement learning for agile maneuvering UAVs

dc.contributor.authorBekar, Can
dc.contributor.authorYuksek, Burak
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2020-03-05T19:36:18Z
dc.date.available2020-03-05T19:36:18Z
dc.date.issued2020-01-05
dc.description.abstractIn this work, we present a high fidelity model based progressive reinforcement learning method for control system design for an agile maneuvering UAV. Our work relies on a simulation-based training and testing environment for doing software-in-the-loop (SIL), hardware-in-the-loop (HIL) and integrated flight testing within photo-realistic virtual reality (VR) environment. Through progressive learning with the high fidelity agent and environment models, the guidance and control policies build agile maneuvering based on fundamental control laws. First, we provide insight on development of high fidelity mathematical models using frequency domain system identification. These models are later used to design reinforcement learning based adaptive flight control laws allowing the vehicle to be controlled over a wide range of operating conditions covering model changes on operating conditions such as payload, voltage and damage to actuators and electronic speed controllers (ESCs). We later design outer flight guidance and control laws. Our current work and progress is summarized in this work.en_UK
dc.identifier.citationCan Bekar U, Yuksek B, Inalhan G. (2020) High fidelity progressive reinforcement learning for agile maneuvering UAVs. In: 2020 AIAA SciTech Forum, 6-10 January 2020 Orlando, Florida, USAen_UK
dc.identifier.urihttps://doi.org/10.2514/6.2020-0898
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15240
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleHigh fidelity progressive reinforcement learning for agile maneuvering UAVsen_UK
dc.typeConference paperen_UK

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