论文标题
用于数据启用数据的预测控制的扩展Kalman过滤器
An Extended Kalman Filter for Data-enabled Predictive Control
论文作者
论文摘要
近年来，涉及数据驱动分析和控制问题的文献已经显着增长。最近的大多数文献都涉及线性时间不变的系统，其中不确定性（如果有）被认为是确定性和有限的。相对较少的关注专门用于随机线性时间不变的系统。作为朝这个方向迈出的第一步，我们建议将最近引入的启用数据的预测控制算法配备基于数据的扩展Kalman滤波器，以利用其他可用的输入输出数据来减少噪声的效果，而无需增加优化过程的计算负载。
The literature dealing with data-driven analysis and control problems has significantly grown in the recent years. Most of the recent literature deals with linear time-invariant systems in which the uncertainty (if any) is assumed to be deterministic and bounded; relatively little attention has been devoted to stochastic linear time-invariant systems. As a first step in this direction, we propose to equip the recently introduced Data-enabled Predictive Control algorithm with a data-based Extended Kalman Filter to make use of additional available input-output data for reducing the effect of noise, without increasing the computational load of the optimization procedure.
