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Neurally Optimized Model Predictive Control for Autonomous Landing of a UAV on a moving platform

Abstract : The uses of UAVs are developing rapidly and are now also used to extend human capabilities. Their control poses several problems that are still open because the system is subject to strong constraints. In this context landing on mobile platform is one of challenging one. In this paper an automated solution for autonomous landing of a UAV on a moving target based on camera vision only is proposed. The method uses a discrete Model Predictive Control (MPC) approach based on Linear Time Invariant (LTI) model. Constraints on inputs, input rates and states are considered and solved using a Neural Optimizer. Software In the Loop (SIL) experiments were performed and proved that the method is valid. Finally a tool-chain for implementation and testing using open-source components only is explained.
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https://hal.archives-ouvertes.fr/hal-03628918
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Submitted on : Sunday, April 3, 2022 - 5:28:18 PM
Last modification on : Tuesday, April 5, 2022 - 3:46:13 AM

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Wojciech Strozecki, Saïd Mammar. Neurally Optimized Model Predictive Control for Autonomous Landing of a UAV on a moving platform. 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), Dec 2021, Xiamen, China. pp.1-6, ⟨10.1109/ICNSC52481.2021.9702230⟩. ⟨hal-03628918⟩

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