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.

翻译：控制系统突破安全约束可能引发潜在风险，导致意外成本或灾难性损害。然而即使在相似任务中，不确定性也普遍存在。本文提出一种新颖的自适应安全控制框架，该框架融合了元学习、贝叶斯模型及控制屏障函数（CBF）方法。具体而言，借助CBF方法，我们通过由前馈神经网络（NN）和贝叶斯输出层构成的统一自适应贝叶斯线性回归（ABLR）模型来学习固有不确定性与外部不确定性。利用元学习技术，基于从历史相似任务收集的数据对神经网络权重及ABLR模型先验进行预训练。对于新控制任务，我们利用少量样本精调元学习模型，并在CBF约束中引入悲观置信界以确保安全控制。此外，我们提供理论准则来保证控制过程中的概率安全性。为验证所提方法，我们分别在多种避障场景中进行对比实验。结果表明，我们的算法显著提升了基于贝叶斯模型的CBF方法，即使在多重不确定性约束下仍能实现高效安全探索。