论文标题
使用SET成员Multistep预测器的线性系统的数据驱动过滤器
Data-driven filtering for linear systems using Set Membership multistep predictors
论文作者
论文摘要
本文介绍了一种新型的数据驱动的直接过滤方法，该方法针对未知的未知和结合测量噪声影响的未知线性时变化系统。提出的技术结合了独立的多步骤预测模型，该模型已确定诉诸于设定的成员资格框架，以完善保证包含真实系统输出的集合。然后将过滤的输出计算为这样的集合中的中心值。通过这样做，该方法实现了准确的输出过滤，并相对于真实的系统输出提供了紧密而最小的误差界限。为了获得这些结果，需要线性程序的在线解决方案。还提出了一种以较低的在线计算成本的修改过滤方法，通过将优化问题的解决方案移至离线初步阶段，以较大的精度范围来获得。在数值示例中评估了建议的方法的性能与基于标准模型的过滤技术的性能。
This paper presents a novel data-driven, direct filtering approach for unknown linear time-invariant systems affected by unknown-but-bounded measurement noise. The proposed technique combines independent multistep prediction models, identified resorting to the Set Membership framework, to refine a set that is guaranteed to contain the true system output. The filtered output is then computed as the central value in such a set. By doing so, the method achieves an accurate output filtering and provides tight and minimal error bounds with respect to the true system output. To attain these results, the online solution of linear programs is required. A modified filtering approach with lower online computational cost is also presented, obtained by moving the solution of the optimization problems to an offline preliminary phase, at the cost of larger accuracy bounds. The performance of the proposed approaches are evaluated and compared with those of standard model-based filtering techniques in a numerical example.
