Differentiable Safe Controller Design through Control Barrier Functions

• URL: http://arxiv.org/abs/2209.10034v1
• Date: Tue, 20 Sep 2022 23:03:22 GMT
• Title: Differentiable Safe Controller Design through Control Barrier Functions
• Authors: Shuo Yang, Shaoru Chen, Victor M. Preciado, Rahul Mangharam
• Abstract summary: Learning-based controllers can show high empirical performance but lack formal safety guarantees. Control barrier functions (CBFs) have been applied as a safety filter to monitor and modify the outputs of learning-based controllers. We propose a safe-by-construction NN controller which employs differentiable CBF-based safety layers.
• Score: 8.283758049749782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees. To address this issue, control barrier functions (CBFs) have been applied as a safety filter to monitor and modify the outputs of learning-based controllers in order to guarantee the safety of the closed-loop system. However, such modification can be myopic with unpredictable long-term effects. In this work, we propose a safe-by-construction NN controller which employs differentiable CBF-based safety layers, and investigate the performance of safe-by-construction NN controllers in learning-based control. Specifically, two formulations of controllers are compared: one is projection-based and the other relies on our proposed set-theoretic parameterization. Both methods demonstrate improved closed-loop performance over using CBF as a separate safety filter in numerical experiments.

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