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Safety-Critical Control via Recurrent Tracking Functions

Published: October 1, 2025 | arXiv ID: 2510.01147v1

By: Jixian Liu, Enrique Mallada

BigTech Affiliations: Johns Hopkins University

Potential Business Impact:

Makes robots safer by letting them learn from mistakes.

Business Areas:
Embedded Systems Hardware, Science and Engineering, Software

This paper addresses the challenge of synthesizing safety-critical controllers for high-order nonlinear systems, where constructing valid Control Barrier Functions (CBFs) remains computationally intractable. Leveraging layered control, we design CBFs in reduced-order models (RoMs) while regulating full-order models' (FoMs) dynamics at the same time. Traditional Lyapunov tracking functions are required to decrease monotonically, but systematic synthesis methods for such functions exist only for fully-actuated systems. To overcome this limitation, we introduce Recurrent Tracking Functions (RTFs), which replace the monotonic decay requirement with a weaker finite-time recurrence condition. This relaxation permits transient deviations of tracking errors while ensuring safety. By augmenting CBFs for RoMs with RTFs, we construct recurrent CBFs (RCBFs) whose zero-superlevel set is control $\tau$-recurrent, and guarantee safety for all initial states in such a set when RTFs are satisfied. We establish theoretical safety guarantees and validate the approach through numerical experiments, demonstrating RTFs' effectiveness and the safety of FoMs.

Country of Origin
πŸ‡ΊπŸ‡Έ United States

Page Count
7 pages

Category
Electrical Engineering and Systems Science:
Systems and Control