  Breaking safety constraints in control systems can lead to potential risks,
resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty
is ubiquitous, even among similar tasks. In this paper, we develop a novel
adaptive safe control framework that integrates meta learning, Bayesian models,
and control barrier function (CBF) method. Specifically, with the help of CBF
method, we learn the inherent and external uncertainties by a unified adaptive
Bayesian linear regression (ABLR) model, which consists of a forward neural
network (NN) and a Bayesian output layer. Meta learning techniques are
leveraged to pre-train the NN weights and priors of the ABLR model using data
collected from historical similar tasks. For a new control task, we refine the
meta-learned models using a few samples, and introduce pessimistic confidence
bounds into CBF constraints to ensure safe control. Moreover, we provide
theoretical criteria to guarantee probabilistic safety during the control
processes. To validate our approach, we conduct comparative experiments in
various obstacle avoidance scenarios. The results demonstrate that our
algorithm significantly improves the Bayesian model-based CBF method, and is
capable for efficient safe exploration even with multiple uncertain
constraints.
