Learning Robust Hybrid Control Barrier Functions for Uncertain Systems

نویسندگان

چکیده

Abstract The need for robust control laws is especially important in safety-critical applications. We propose hybrid barrier functions as a means to synthesize that ensure safety. Based on this notion, we formulate an optimization problem learning from data. identify sufficient conditions the data such feasibility of ensures correctness learned functions. Our techniques allow us safely expand region attraction compass gait walker subject model uncertainty.

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ژورنال

عنوان ژورنال: IFAC-PapersOnLine

سال: 2021

ISSN: ['2405-8963', '2405-8971']

DOI: https://doi.org/10.1016/j.ifacol.2021.08.465