论文标题
具有不确定性的非线性控制系统的强大控制屏障功能：一种基于二重性的方法
Robust Control Barrier Functions for Nonlinear Control Systems with Uncertainty: A Duality-based Approach
论文作者
论文摘要
本文研究了控制器的设计，以确保在漂移和控制矢量场中具有参数不确定性的非线性控制仿射系统的稳定性和安全性。为此，我们介绍了新颖的鲁棒控制屏障功能（RCBF）和鲁棒控制Lyapunov功能（RCLF），这些功能（RCLF）促进了使用Quadratic编程存在参数不确定性的情况下在存在参数不确定性的情况下合成安全 - 临界控制器的。由于系统不确定性上的初始界限可能是高度保守的，因此我们提出了一种数据驱动的方法，可以使用在线收集的输入输出数据来降低此类界限。特别是，我们利用了一种整体设置会员身份识别算法，该算法迭代地缩小了可能的系统参数，并保证了学习过程中的稳定性和安全性。在两个数值示例中说明了开发方法的功效。
This paper studies the design of controllers that guarantee stability and safety of nonlinear control affine systems with parametric uncertainty in both the drift and control vector fields. To this end, we introduce novel classes of robust control barrier functions (RCBF) and robust control Lyapunov functions (RCLF) that facilitate the synthesis of safety-critical controllers in the presence of parametric uncertainty using quadratic programming. Since the initial bounds on the system uncertainty may be highly conservative, we present a data-driven approach to reducing such bounds using input-output data collected online. In particular, we leverage an integral set-membership identification algorithm that iteratively shrinks the set of possible system parameters online and guarantees stability and safety during learning. The efficacy of the developed approach is illustrated on two numerical examples.
