Learning Local Control Barrier Functions for Safety Control of Hybrid Systems

• URL: http://arxiv.org/abs/2401.14907v1
• Date: Fri, 26 Jan 2024 14:38:43 GMT
• Title: Learning Local Control Barrier Functions for Safety Control of Hybrid Systems
• Authors: Shuo Yang, Yu Chen, Xiang Yin, Rahul Mangharam
• Abstract summary: Safety is a primary concern for hybrid robotic systems. Existing safety-critical control approaches for hybrid systems are either computationally inefficient, detrimental to system performance, or limited to small-scale systems. We propose a learningenabled approach to construct local Control Barrier Functions (CBFs) to guarantee the safety of a wide class of nonlinear hybrid dynamical systems.
• Score: 11.57209279619218
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hybrid dynamical systems are ubiquitous as practical robotic applications often involve both continuous states and discrete switchings. Safety is a primary concern for hybrid robotic systems. Existing safety-critical control approaches for hybrid systems are either computationally inefficient, detrimental to system performance, or limited to small-scale systems. To amend these drawbacks, in this paper, we propose a learningenabled approach to construct local Control Barrier Functions (CBFs) to guarantee the safety of a wide class of nonlinear hybrid dynamical systems. The end result is a safe neural CBFbased switching controller. Our approach is computationally efficient, minimally invasive to any reference controller, and applicable to large-scale systems. We empirically evaluate our framework and demonstrate its efficacy and flexibility through two robotic examples including a high-dimensional autonomous racing case, against other CBF-based approaches and model predictive control.

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