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[2602.20076] Robust Taylor-Lagrange Control for Safety-Critical Systems
Summary
The paper presents a robust Taylor-Lagrange Control (rTLC) method for safety-critical systems, addressing the feasibility preservation problem inherent in existing control methods.
Why It Matters
As safety-critical systems become increasingly prevalent in areas like autonomous vehicles and robotics, ensuring their reliability is paramount. The rTLC method enhances safety by providing a more effective control mechanism that mitigates feasibility issues, which is crucial for real-time applications.
Key Takeaways
- The rTLC method improves upon traditional Control Barrier Functions by addressing feasibility preservation issues.
- It utilizes a higher-order Taylor expansion to enhance safety function representation.
- The method simplifies implementation with only one hyper-parameter related to discretization time.
- Demonstrated effectiveness through an adaptive cruise control application.
- Offers a comparative analysis with existing safety-critical control methods.
Electrical Engineering and Systems Science > Systems and Control arXiv:2602.20076 (eess) [Submitted on 23 Feb 2026] Title:Robust Taylor-Lagrange Control for Safety-Critical Systems Authors:Wei Xiao, Christos Cassandras, Anni Li View a PDF of the paper titled Robust Taylor-Lagrange Control for Safety-Critical Systems, by Wei Xiao and Christos Cassandras and Anni Li View PDF HTML (experimental) Abstract:Solving safety-critical control problem has widely adopted the Control Barrier Function (CBF) method. However, the existence of a CBF is only a sufficient condition for system safety. The recently proposed Taylor-Lagrange Control (TLC) method addresses this limitation, but is vulnerable to the feasibility preservation problem (e.g., inter-sampling effect). In this paper, we propose a robust TLC (rTLC) method to address the feasibility preservation problem. Specifically, the rTLC method expands the safety function at an order higher than the relative degree of the function using Taylor's expansion with Lagrange remainder, which allows the control to explicitly show up at the current time instead of the future time in the TLC method. The rTLC method naturally addresses the feasibility preservation problem with only one hyper-parameter (the discretization time interval size during implementation), which is much less than its counterparts. Finally, we illustrate the effectiveness of the proposed rTLC method through an adaptive cruise control problem, and compare it with existing ...
