A Trisection Algorithm for Estimating Distance Measures for Strong Observability and Strong Detectability

• 成果类型: Article
• 署名作者: Falkensteiner, Roland; Seeber, Richard; Horn, Martin
• 署名单位: Graz University of Technology; Graz University of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3142120
• 发表日期: 2023
• 页码: 478-485
• 关键词: Linear systems; mechatronics; observability measures; optimization algorithms

摘要

For reliably analyzing the properties of strong observability and strong detectability of a system, continuous distance measures can be used. When calculating these measures, it is necessary to find the global minimum of a nonconvex target function. The main contribution of this article is an optimization algorithm that guarantees to find this global minimum in a fast and efficient way by exploiting the special structure of the optimization problem. Using this optimization algorithm, the distance measures can be reliably calculated. The numerical properties and the usefulness of the algorithm in practical applications are illustrated by means of a numerical example.