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Learning Feasibility Constraints for Control Barrier Functions
It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs).
Feasibility-Guided Learning for Robust Control in Constrained Optimal Control Problems
In this paper, we further improve the feasibility robustness (i. e., feasibility maintenance in the presence of time-varying and unknown unsafe sets) through the definition of a High Order CBF (HOCBF) that works for arbitrary relative degree constraints; this is achieved by a proposed feasibility-guided learning approach.
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