论文标题
模型参考高斯过程回归：数据驱动的输出反馈控制器
Model Reference Gaussian Process Regression: Data-Driven Output Feedback Controller
论文作者
论文摘要
使用高斯工艺回归的数据驱动控制最近引起了很多关注。在这种方法中，高斯过程回归的系统识别主要是基于模型的控制器设计。但是，高斯过程回归的结果通常太复杂了，无法应用常规的控制设计，这使得数值设计（例如在许多情况下使用的模型预测性控制）。为了克服限制，我们的想法是对植物的倒数进行高斯过程回归，并具有相同的输入/输出数据以进行常规回归。通过反向，可以设计模型参考控制器而无需求助于数值控制方法。本文认为具有相对程度的最小相位的单输入单输出（SISO）离散时间非线性系统。强调的是，模型参考高斯过程回归控制器是直接从没有系统识别的预采用的输入/输出数据设计的。
Data-driven controls using Gaussian process regression have recently gained much attention. In such approaches, system identification by Gaussian process regression is mostly followed by model-based controller designs. However, the outcomes of Gaussian process regression are often too complicated to apply conventional control designs, which makes the numerical design such as model predictive control employed in many cases. To overcome the restriction, our idea is to perform Gaussian process regression to the inverse of the plant with the same input/output data for the conventional regression. With the inverse, one can design a model reference controller without resorting to numerical control methods. This paper considers single-input single-output (SISO) discrete-time nonlinear systems of minimum phase with relative degree one. It is highlighted that the model reference Gaussian process regression controller is designed directly from pre-collected input/output data without system identification.
