Safe Learning Control with Optimality and Stability Guarantees
On the map: Deep reinforcement learning methods → Safe and constrained RL
Abstract (arXiv)
Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to guarantee both safety and performance in learning-based control problems is an interesting yet challenging issue. This paper aims to enhance system performance with a safety guarantee by solving reinforcement learning (RL)-based optimal control problems for nonlinear systems subject to high-relative-degree state constraints and unknown time-varying disturbance/actuator faults. A new type of control barrier functions (CBFs), termed high-order reciprocal-based control barrier function, is proposed to handle high-relative-degree constraints, which extends the design of CBFs to enforce robust safety without knowing the disturbance bound. The concept of gradient similarity is proposed to quantify the relationship between safety and performance. Finally, gradient manipulation and adaptive mechanisms are introduced in the model-based safe RL framework to enhance the performance with a safety guarantee. Two simulation examples illustrate the efficacy of the proposed algorithms.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 39 references that are in the map. First posted on arXiv 26 Jan 2025.
Builds on
14 of 39 references.
Closest ancestors · 1
Same direction, newest first
Other references · 13
- 2021Convex optimizationcited by 2,515
- 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 287
- 2022Safe Reinforcement Learning Using Black-Box Reachability Analysiscited by 26
- 2021Geometrically Constrained Trajectory Optimization for Multicopterscited by 19
- 2020Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systemscited by 15
- 2022SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Controlcited by 15
- 2021Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functionscited by 11
- 2021High-order Barrier Functions: Robustness, Safety and Performance-Critical Controlcited by 8
- 2022Safe Control Synthesis with Uncertain Dynamics and Constraintscited by 7
- 2024Robust Control Barrier Functions using Uncertainty Estimation with Application to Mobile Robotscited by 6
- 2023On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approachcited by 4
- 2023Lagrangian-based online safe reinforcement learning for state-constrained systemscited by 2
1 more
Built on by
Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2025Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions
- 2025Multi-Constraint Safe Reinforcement Learning via Closed-form Solution for Log-Sum-Exp Approximation of Control Barrier Functions
- 2025A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
- 2026Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning
- 2024Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
- 2024Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
- 2024Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning
- 2025Learning State-Action Control Barrier Functions for Model-Free Control under Constraints