Robust Gain-Scheduled Estimation With Dynamic D-Scalings

• 成果类型: Article
• 署名作者: Holicki, Tobias; Scherer, Carsten W.
• 署名单位: University of Stuttgart
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3052751
• 发表日期: 2021
• 页码: 5592-5598
• 关键词: estimation; dynamic scheduling; uncertainty; Linear systems; Symmetric matrices; Linear matrix inequalities; Measurement uncertainty; Dynamic multipliers; estimation; gain scheduling; Linear matrix inequalities (LMIs); Robust control

摘要

We provide a convex solution to the robust gain-scheduled estimation problem based on integral quadratic constraints with dynamic D-scalings for both the uncertain and the scheduled component. This closes an important gap since, so far, merely static scalings could be used for the scheduled component in estimation problems. To this end, we provide novel synthesis criteria in terms of linear matrix inequalities for the design of nominal gain-scheduled estimators that allow for a direct combination with available results on robust estimation. We illustrate the benefit of our design approach by means of a numerical example.