Exploiting input sparsity for joint state/input moving horizon estimation
Exploiting input sparsity for joint state/input moving horizon estimation
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DOI：
10.1016/j.ymssp.2017.08.024
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发表时间：
2018-02-15
影响因子：
8.4
通讯作者：
Desmet, W.
中科院分区：
文献类型：
--
作者：
Kirchner, M.;Croes, J.;Desmet, W.
本文提出了一种新的时域方法用于机械系统的联合状态/输入估计。新奇包括利用压缩感知（CS）的原理，在一个移动的地平线估计（MHE），允许观察大量的输入位置给出一个小的测量。现有的技术的特点是固有的局限性时，估计多个输入位置，由于可观测性下降。此外，CS不要求输入以慢动态为特征，这是用于输入建模的其他现有技术的要求。在新的方法中，称为压缩感知移动时域估计器（CS-MHE），MHE的能力，最大限度地减少噪声，同时相关的模型与测量值是丰富的一个范数优化，以促进稀疏的解决方案的输入估计。一个数值例子表明，CS-MHE允许一个未知的输入估计的幅度，时间和位置，利用假设的输入是稀疏的时间和空间。最后，一个实验装置作为验证案例。(C)2017爱思唯尔有限公司版权所有
This paper proposes a novel time domain approach for joint state/input estimation of mechanical systems. The novelty consists of exploiting compressive sensing (CS) principles in a moving horizon estimator (MHE), allowing the observation of a large number of input locations given a small set of measurements. Existing techniques are characterized by intrinsic limitations when estimating multiple input locations, due to an observability decrease. Moreover, CS does not require an input to be characterized by a slow dynamics, which is a requirement of other state of the art techniques for input modeling. In the new approach, called compressive sensing-moving horizon estimator (CS-MHE), the capability of the MHE of minimizing the noise while correlating a model with measurements is enriched with an-norm optimization in order to promote a sparse solution for the input estimation. A numerical example shows that the CS-MHE allows for an unknown input estimation in terms of magnitude, time and location, exploiting the assumption that the input is sparse in time and space. Finally, an experimental setup is presented as validation case. (C) 2017 Elsevier Ltd. All rights reserved.