论文标题
固定时间稳定适应定律，用于在参数不确定性下进行安全关键控制
A Fixed-Time Stable Adaptation Law for Safety-Critical Control under Parametric Uncertainty
论文作者
论文摘要
我们提出了一种新的技术，用于解决一类应受参数模型不确定性的一般非线性，控制型系统的安全控制问题。调用Lyapunov分析和固定时间稳定性的概念（FXTS），我们引入了一个参数适应定律，该法律保证了系统动力学中未知参数的估计值在固定时值的真实值的收敛性，独立于初始参数估计误差。然后，我们以强大的，自适应的控制屏障功能（RACBF）二次程序合成适应定律，尽管考虑了模型的不确定性，但基于二次的二次程序来计算安全控制输入。为了证实我们的结果，我们对此结果进行了比较案例研究，与文献在不确定性下对安全控制的其他最新方法进行了比较，并通过在自动机动超过场景的背景下强调我们方法的价值。
We present a novel technique for solving the problem of safe control for a general class of nonlinear, control-affine systems subject to parametric model uncertainty. Invoking Lyapunov analysis and the notion of fixed-time stability (FxTS), we introduce a parameter adaptation law which guarantees convergence of the estimates of unknown parameters in the system dynamics to their true values within a fixed-time independent of the initial parameter estimation error. We then synthesize the adaptation law with a robust, adaptive control barrier function (RaCBF) based quadratic program to compute safe control inputs despite the considered model uncertainty. To corroborate our results, we undertake a comparative case study on the efficacy of this result versus other recent approaches in the literature to safe control under uncertainty, and close by highlighting the value of our method in the context of an automobile overtake scenario.
