论文标题
高阶稳健自适应控制屏障功能和指数稳定自适应控制Lyapunov功能
High Order Robust Adaptive Control Barrier Functions and Exponentially Stabilizing Adaptive Control Lyapunov Functions
论文作者
论文摘要
本文研究了利用数据驱动的自适应控制技术来确保具有高度相对程度的非线性非线性系统的稳定性和安全性。我们首先介绍了高阶鲁棒自适应控制屏障功能（HO-RACBF）的概念，作为计算控制策略的一种手段，以确保面对参数模型不确定性，确保高度相对度安全约束满足。开发的方法可以通过最初考虑所有可能的参数实现来确保安全性，但可以自适应地降低参数估计的不确定性，从而利用在线记录的数据。然后，我们介绍了指数稳定的自适应控制Lyapunov函数（ES-ACLF）的概念，该函数利用与HO-RACBF控制器相同的数据来保证系统轨迹的指数收敛性。开发的HO-RACBF和ES-ACLF在二次编程框架中统一，其功效是通过两个数值示例展示的，据我们所知，现有的自适应控制屏障功能技术无法解决。
This paper studies the problem of utilizing data-driven adaptive control techniques to guarantee stability and safety of uncertain nonlinear systems with high relative degree. We first introduce the notion of a High Order Robust Adaptive Control Barrier Function (HO-RaCBF) as a means to compute control policies guaranteeing satisfaction of high relative degree safety constraints in the face of parametric model uncertainty. The developed approach guarantees safety by initially accounting for all possible parameter realizations but adaptively reduces uncertainty in the parameter estimates leveraging data recorded online. We then introduce the notion of an Exponentially Stabilizing Adaptive Control Lyapunov Function (ES-aCLF) that leverages the same data as the HO-RaCBF controller to guarantee exponential convergence of the system trajectory. The developed HO-RaCBF and ES-aCLF are unified in a quadratic programming framework, whose efficacy is showcased via two numerical examples that, to our knowledge, cannot be addressed by existing adaptive control barrier function techniques.
