Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation

• URL: http://arxiv.org/abs/2401.05629v2
• Date: Fri, 01 Nov 2024 21:46:03 GMT
• Title: Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation
• Authors: Lakshmideepakreddy Manda, Shaoru Chen, Mahyar Fazlyab,
• Abstract summary: Control Barrier Functions (CBFs) provide an elegant framework for constraining nonlinear control system dynamics. We introduce a novel self-supervised learning framework to comprehensively address these challenges. We validate our approach on a 2D double integrator (DI) system and a 7D fixed-wing aircraft system.
• Score: 5.62479170374811
• License:
• Abstract: Control Barrier Functions (CBFs) provide an elegant framework for constraining nonlinear control system dynamics to remain within an invariant subset of a designated safe set. However, identifying a CBF that balances performance-by maximizing the control invariant set-and accommodates complex safety constraints, especially in systems with high relative degree and actuation limits, poses a significant challenge. In this work, we introduce a novel self-supervised learning framework to comprehensively address these challenges. Our method begins with a Boolean composition of multiple state constraints that define the safe set. We first construct a smooth function whose zero superlevel set forms an inner approximation of this safe set. This function is then combined with a smooth neural network to parameterize the CBF candidate. To train the CBF and maximize the volume of the resulting control invariant set, we design a physics-informed loss function based on a Hamilton-Jacobi Partial Differential Equation (PDE). We validate the efficacy of our approach on a 2D double integrator (DI) system and a 7D fixed-wing aircraft system (F16).

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