Sampling Strategies for Data-Driven Inference of Input-Output System Properties

• 成果类型: Article
• 署名作者: Koch, Anne; Montenbruck, Jan Maximilian; Allgower, Frank
• 署名单位: University of Stuttgart
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2994894
• 发表日期: 2021
• 页码: 1144-1159
• 关键词: Data-based systems analysis; identification for control; Linear systems; Machine Learning; optimization

摘要

Due to their relevance in controller design, we consider the problem of determining the L-2-gain, passivity properties, and conic relations of an input-output system. While, in practice, the input-output relation is often undisclosed, input-output data tuples can be sampled by performing (numerical) experiments. Hence, we present sampling strategies for discrete time and continuous time linear time-invariant systems to iteratively determine the L-2-gain, the shortage of passivity and the cone with minimal radius that the input-output relation is confined to. These sampling strategies are based on gradient dynamical systems and saddle point flows to solve the reformulated optimization problems, where the gradients can be evaluated from only input-output data samples. This leads us to evolution equations, whose convergence properties are then discussed in continuous time and discrete time.