Data-Driven Permissible Safe Control with Barrier Certificates
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要