Pick-to-Learn methodology calibrates flight control policy using minimal scenarios

This paper introduces the Pick-to-Learn (P2L) methodology for calibrating Model Predictive Control (MPC) policies, demonstrated through an aircraft navigation problem. The P2L procedure identified two key wind scenarios from an initial dataset of 400, which were then used to optimize the MPC policy's hyperparameters. The resulting policy successfully avoided a low-connectivity zone across all scenarios and met a probabilistic risk bound of 4.8% at a 1-10^-5 confidence level. AI

IMPACT Introduces a novel methodology for optimizing control policies with limited data, potentially applicable to various autonomous systems.

RANK_REASON The item is an academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• Influence Flower
• model predictive control
• Pick-to-Learn
• ScienceCast

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