论文标题
通过近似非线性取消数据从数据中学习控制器
Learning Controllers from Data via Approximate Nonlinearity Cancellation
论文作者
论文摘要
我们介绍了一种处理非线性系统数据驱动控制设计的方法。我们通过（近似）非线性取消来设计控制器的条件。这些条件采用与数据相关的半明确程序的紧凑形式。该方法返回控制器，即使数据受到扰动，也可以经过认证以稳定系统并在执行控制任务期间会影响系统的动态，在这种情况下，提供了对稳健阳性不变的集合的估计。
We introduce a method to deal with the data-driven control design of nonlinear systems. We derive conditions to design controllers via (approximate) nonlinearity cancellation. These conditions take the compact form of data-dependent semi-definite programs. The method returns controllers that can be certified to stabilize the system even when data are perturbed and disturbances affect the dynamics of the system during the execution of the control task, in which case an estimate of the robustly positively invariant set is provided.
