Data-Driven Permissible Safe Control with Barrier Certificates

2024-04-30 · Rayan Mazouz, John Skovbekk, Frederik Baymler Mathiesen, Eric Frew, Luca Laurenti, Morteza Lahijanian

This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is learning the dynamics of the system via Gaussian process (GP) regression and obtaining probabilistic errors for this estimate. Then, we develop an algorithm for constructing piecewise stochastic barrier functions to find a maximal permissible strategy set using the learned GP model, which is based on sequentially pruning the worst controls until a maximal set is identified. The permissible strategies are guaranteed to maintain probabilistic safety for the true system. This is especially important for learning-enabled systems, because a rich strategy space enables additional data collection and complex behaviors while remaining safe. Case studies on linear and nonlinear systems demonstrate that increasing the size of the dataset for learning the system grows the permissible strategy set.

📄 PDF Abstract BibTeX arXiv:2405.00136

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

Gaussian Process Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for…

Pruning 설명 없음

Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions

2022-05-22 · Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu

Control barrier functions are widely used to synthesize safety-critical controls. The existence of Gaussian-type noise may lead to unsafe actions and result in severe consequences. While studies are widely done in safety…

Data-Driven Time-Varying Control Barrier Functions for Adaptive Safe-Set Learning with Online Decremental Support Vector Machines

2026-08-19 · Shawon Dey, Michael Budihartono, Hever Moncayo arxiv

Mission-critical intelligent systems often operate under time-varying limitations that reduce control authority and change the admissible safe operating envelope. In such settings, a safety certificate learned under nomi…

Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey

2024-04-22 · Maeva Guerrier, Hassan Fouad, Giovanni Beltrame

Reinforcement learning is a powerful technique for developing new robot behaviors. However, typical lack of safety guarantees constitutes a hurdle for its practical application on real robots. To address this issue, safe…

Lifelong learning reinforcement-learning Reinforcement Learning Safe Reinforcement Learning

Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions

2021-04-06 · Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu

Control barrier functions have been widely used for synthesizing safety-critical controls, often via solving quadratic programs. However, the existence of Gaussian-type noise may lead to unsafe actions and result in seve…

Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions

2023-10-01 · Anni Li, Christos G. Cassandras, Wei Xiao

This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predi…