文章基本信息

标题：Ensuring Stability and Fidelity of Recursively Identified Control-Relevant Models
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作者：Iman Hajizadeh ; Mudassir Rashid ; Ali Cinar 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：927-932
DOI：10.1016/j.ifacol.2018.09.075
语种：English
出版社：Elsevier
摘要：AbstractIn this work, a recursive system identification algorithm is extended to improve reliability and better handle stochastic disturbances, measurement noise, and other adverse phenomena. The proposed approach involves the modification of the recursive predictor-based subspace identification (PBSID) algorithm to incorporate constraints on the fidelity and accuracy of the identified models, correctness of the sign of the input-to-output gains, and the integration of heuristics to ensure stability of the recursively identified models. The efficacy of the proposed approach is demonstrated using case studies involving the modeling of time-varying glucose–insulin dynamics.
关键词：Keywordsrecursive system identificationsubspace identificationstate-space modelssystem stabilityartificial pancreas