<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents a new formulation for set-membership parameter estimation of fractional systems. In such a context, the error between the measured data and the output model is supposed to be unknown but bounded with <italic>a priori</italic> known bounds. The bounded error is specified over measurement noise, rather than over an equation error, which is mainly motivated by experimental considerations. The proposed approach is based on the optimal bounding ellipsoid algorithm for linear output-error fractional models. A numerical example is presented to show effectiveness and discuss results.</p></abstract>

This paper presents a new formulation for set-membership parameter estimation of fractional systems. In such a context, the error between the measured data and the output model is supposed to be unknown but bounded with a priori known bounds. The bounded error is specified over measurement noise, rather than over an equation error, which is mainly motivated by experimental considerations. The proposed approach is based on the optimal bounding ellipsoid algorithm for linear output-error fractional models. A numerical example is presented to show effectiveness and discuss results.

Recursive set membership estimation for output–error fractional models with unknown–but–bounded errors

Research Laboratory of Modeling, Analysis and Control of Systems, National Engineering School of Gabes (ENIG), University of Gabes, Omar Ibn el Khattab St., 6029 Gabes,

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents a new formulation for set-membership parameter estimation of fractional systems. In such a context, the error between the measured data and...

具有未知但有界误差的输出误差分数模型的递归集员估计

在线发布:2016年9月29日

页面范围： 543 - 553

收到:2015年7月15日

认可的:2016年3月7日

内政部： https://doi.org/10.1515/amcs-2016-0038

关键词
分数微积分,设置成员身份,估计,未知但有界误差

©2016 Messaoud Amairi，由De Gruyter Open出版

本作品根据Creative Commons Attribution-NonCommercial-NoDerivatives 3.0许可证授权。