论文标题
不确定线性系统的合成安全控制器：直接数据驱动的方法
Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach
论文作者
论文摘要
在本文中，我们为未知的线性系统合成安全控制器提供了直接的数据驱动方法，该系统受未知和结合的干扰影响，其中不需要识别未知模型。首先，我们提出了$γ$ -Robust安全不变（$γ$ -RSI）及其相关的状态反馈控制器的概念，可用于执行不变性属性。然后，我们根据线性矩阵不等式（LMI）的凸优化问题来制定这些集合的数据驱动计算，作为约束，可以根据系统的单个输入状态轨迹收集的有限数量数据来求解。为了显示所提出的方法的有效性，我们将结果应用于4维倒立的摆。
In this paper, we provide a direct data-driven approach to synthesize safety controllers for unknown linear systems affected by unknown-but-bounded disturbances, in which identifying the unknown model is not required. First, we propose a notion of $γ$-robust safety invariant ($γ$-RSI) sets and their associated state-feedback controllers, which can be applied to enforce invariance properties. Then, we formulate a data-driven computation of these sets in terms of convex optimization problems with linear matrix inequalities (LMI) as constraints, which can be solved based on a finite number of data collected from a single input-state trajectory of the system. To show the effectiveness of the proposed approach, we apply our results to a 4-dimensional inverted pendulum.
