Learning Robust Hybrid Control Barrier Functions for Uncertain Systems

• URL: http://arxiv.org/abs/2101.06492v1
• Date: Sat, 16 Jan 2021 17:53:35 GMT
• Title: Learning Robust Hybrid Control Barrier Functions for Uncertain Systems
• Authors: Alexander Robey, Lars Lindemann, Stephen Tu, and Nikolai Matni
• Abstract summary: We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
• Score: 68.30783663518821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.

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