Co-design of reduced-order models and observers from thermo-fluid data. (March 2022)

• Record Type: Journal Article
• Authors: Vijayshankar, Sanjana; Chakrabarty, Ankush; Grover, Piyush; Nabi, Saleh
• Is Part Of: IFAC journal of systems and control. Volume 19(2022)
• Publication Date: 2022-03
• Subjects: Co-design -- State estimation -- Data-driven methods -- Linear matrix inequalities -- Partial differential equations -- Turbulent flows -- Model order reduction; Automatic control -- Periodicals; Relay control systems -- Periodicals; Embedded computer systems -- Periodicals; Feedback control systems -- Periodicals; Artificial intelligence -- Periodicals; Artificial intelligence; Automatic control; Embedded computer systems; Feedback control systems; Relay control systems; Electronic journals; Periodicals; 629.89
• DOI: 10.1016/j.ifacsc.2021.100181
• Languages: English
• ISSNs: 2468-6018

Abstract

Abstract: This paper presents a method of co-design of models and observers for buoyancy-driven turbulent flows. Recent work on data-driven techniques for estimating turbulent flows typically involve obtaining a dynamical model using Dynamical Mode Decomposition (DMD) and using the model to design estimators. Unfortunately, such a sequential design could result in state-space models that do not possess control-theoretic properties (such as detectability) that ensure guaranteed performance of the observer. In this paper, we propose semi-definite programs (SDPs) that allow us to simultaneously construct observer gains, along with DMD models which exhibit desired properties. Since DMD models for turbulent flows are typically high-dimensional, we provide a tractable algorithm for solving the high-dimensional SDP. We demonstrate the potential of our proposed approach on an industrial application using real-world data, and illustrate that the co-design significantly outperforms sequential design.