Safety-Critical Control

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Safety-critical control studies how to make robots, vehicles, industrial processes, and other dynamical systems obey hard limits while still completing tasks. Common tools include control barrier functions and safety filters, reachability and invariant-set methods, model predictive control, and runtime-assurance shields that intervene on learned or nominal policies. Work also combines these guarantees with reinforcement learning, neural models, and calibrated uncertainty methods such as conformal prediction, with evaluations in navigation, manipulation, autonomous driving, and aerospace. A central challenge is balancing formal guarantees with model error, partial observation, actuator limits, and real-time computation: conservative filters can become infeasible or impede progress, while empirical success alone does not establish safety.

Latest papers

129 papers19 papers in the last four weeks, up 6% on the four weeks before. 0.1% of all new papers.

Robust Taylor-Lagrange Control for Safety-Critical Systems with Guaranteed Feasibility
Feb 23, 2026Wei Xiao, Christos Cassandras, Anni LiSafety-Critical Control Robust Control
Robotics Engineering Department at Worcester Polytechnic Institute and MIT CSAIL, USA · division of Systems Engineering at Boston University, USA · Electrical and Computer Engineering Department at The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
Dec 29, 2025Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi +5Language Model-Based Control Safety-Critical Control
The Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA · Department of Nuclear Engineering, Hanyang University, Republic of Korea · University of Texas - El Paso, University Ave, El Paso, USA +4
SB-TRPO: Towards Safe Reinforcement Learning with Hard Constraints
Dec 29, 2025Dominik Wagner, Ankit Kanwar, Luke OngSafety-Critical Control Policy Optimization
NTU Singapore · Sony Corporation
Least Restrictive Hyperplane Control Barrier Functions
Oct 21, 2025Mattias Trende, Petter ÖgrenSafety-Critical Control Control Barrier Functions
The authors are with the Robotics, Perception and Learning Lab., School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), SE-100 44 Stockholm, Sweden
Stochastic Neural Control Barrier Functions
Jun 26, 2025Hongchao Zhang, Manan Tayal, Jackson Cox +3Control Barrier Functions Safety-Critical Control
Electrical and Systems Engineering Department, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO 63130 USA · Center for Cyber-Physical Systems, Indian Institute of Science (IISc), Bengaluru.
Captivity-Escape Games as a Means for Safety in Online Motion Generation
Jun 2, 2025Christopher Bohn, Manuel Hess, Sören HohmannTrajectory Generation Safety-Critical Control
Institute of Control Systems, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Learning-based Adaptive Safety-Critical Control With Evolving Unsafe Regions
Mar 19, 2025Songqiao Hu, Zidong Wang, Zeyi Liu +2Safety-Critical Control Control Barrier Functions
Department of Automation and the Institute for Embodied Intelligence and Robotics, Tsinghua University, Beijing 100084, China · Department of Computer Science, Brunel University London, UB8 3PH Uxbridge, U.K. · State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
No Undesirable Equilibria, No Collisions: Combining Modulation and Control Barrier Functions for Liveness and Safety in Dynamic Collision Avoidance
Feb 20, 2025Yifan Xue, Nadia FigueroaControl Barrier Functions Safety-Critical Control
the Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104 USA
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control
Feb 4, 2025Peiyan Hu, Xiaowei Qian, Wenhao Deng +8Diffusion Model Guidance Safety-Critical Control
Work done as an intern at Westlake University · Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · School of Engineering, Westlake University, Hangzhou, China +2

Feb 23, 2026J/K move · Enter open · S save

Robust Taylor-Lagrange Control for Safety-Critical Systems with Guaranteed Feasibility

Wei Xiao, Christos Cassandras, Anni Li

Robotics Engineering Department at Worcester Polytechnic Institute and MIT CSAIL, USA · division of Systems Engineering at Boston University, USA · Electrical and Computer Engineering Department at The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

Safety-Critical Control Robust Control

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The Control Barrier Function (CBF) method has been widely adopted in solving safety-critical control problems. 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 by providing a necessary and sufficient condition, but it is still vulnerable to the feasibility preservation problem (e.g., due to the inter-sampling effect when time is discretized). In this paper, we propose a robust TLC (rTLC) method to address the feasibility preservation problem and show how to use it to solve optimal control problems. Specifically, the rTLC method expands the safety function to an order higher than its relative degree using Taylor's expansion with Lagrange remainder, which allows the control to explicitly show up at the current time instead of a future time in the original TLC method. This implies that the safety constraint remains satisfied over the entire discretization time interval that follows. Moreover, the rTLC method naturally addresses the feasibility preservation problem with only a single hyper-parameter (the discretization time interval size during implementation), compared to significantly more parameters for its counterparts. Finally, we illustrate the effectiveness of the proposed rTLC method through an adaptive cruise control problem, and compare it with existing safety-critical control methods.