Synthesizing Control Barrier Functions With Artificial Potential Fields for Safe Reinforcement Learning

Changchun Hua; Hainan Zhang; Jiannan Chen; Xi Luo

doi:10.1109/tsmc.2025.3551291

ScienceGate Book Chapters

JOURNAL ARTICLE

Synthesizing Control Barrier Functions With Artificial Potential Fields for Safe Reinforcement Learning

Changchun Hua Hainan Zhang Jiannan Chen Xi Luo

Year: 2025 Journal: IEEE Transactions on Systems Man and Cybernetics Systems Pages: 1-10 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/tsmc.2025.3551291

Get Full-Text PDF Get Analytical Report

Keywords:

Reinforcement learning Control (management) Artificial intelligence Computer science

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.19

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Smart Grid Security and Resilience

Physical Sciences → Engineering → Control and Systems Engineering

Simulation Techniques and Applications

Social Sciences → Decision Sciences → Management Science and Operations Research

Real-time simulation and control systems

Physical Sciences → Engineering → Control and Systems Engineering

Synthesizing Control Barrier Functions With Artificial Potential Fields for Safe Reinforcement Learning

Metrics

Topics

Related Documents

Synthesizing Control Barrier Functions With Feasible Region Iteration for Safe Reinforcement Learning

Safe Reinforcement Learning Using Robust Control Barrier Functions

Reinforcement Learning with Probabilistically Safe Control Barrier Functions for Ramp Merging

Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions

Safe exploration in model-based reinforcement learning using control barrier functions