论文标题
使用控制Lyapunov功能和控制屏障功能，在模型不确定性下进行安全关键控制的强化学习
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions
论文作者
论文摘要
在本文中，通过数据驱动的方法解决了安全 - 关键控制中模型不确定性的问题。为此，我们利用了基于名义模型的输入 - 仪线性控制器的结构，以及基于控制屏障函数和控制Lyapunov函数的二次二次程序（CBF-CLF-QP）。具体而言，我们提出了一个新颖的增强学习框架，该框架学习了CBF和CLF约束中存在的模型不确定性，以及二次程序中的其他控制型动态约束。训练有素的策略与基于名义模型的CBF-CLF-QP结合使用，从而导致基于增强学习的CBF-CLF-QP（RL-CBF-CLF-QP），这解决了安全约束中模型不确定性的问题。通过在一个步骤预览的随机间隔踏板上行走的非线性双足机器人在不足的非线性双足机器人上测试所提出的方法的性能，并在模型不确定性下获得稳定且安全的行走。
In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-ouput linearization controller based on a nominal model along with a Control Barrier Function and Control Lyapunov Function based Quadratic Program (CBF-CLF-QP). Specifically, we propose a novel reinforcement learning framework which learns the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program. The trained policy is combined with the nominal model-based CBF-CLF-QP, resulting in the Reinforcement Learning-based CBF-CLF-QP (RL-CBF-CLF-QP), which addresses the problem of model uncertainty in the safety constraints. The performance of the proposed method is validated by testing it on an underactuated nonlinear bipedal robot walking on randomly spaced stepping stones with one step preview, obtaining stable and safe walking under model uncertainty.
