A System Parametrization for Direct Data-Driven Analysis and Control with Error-in-Variables
In this paper, we present a new parametrization to perform direct data-driven analysis and controller synthesis for the error-in-variables case. To achieve this, we employ the Sherman-Morrison-Woodbury formula to transform the problem into a linear fractional transformation (LFT) with unknown measurement errors and disturbances as uncertainties. For bounded uncertainties, we apply robust control techniques to derive a guaranteed upper bound on the H2-norm of the unknown true system. To this end, a single semidefinite program (SDP) needs to be solved, with complexity that is independent of the amount of data. Furthermore, we exploit the signal-to-noise ratio to provide a data-dependent condition, that characterizes whether the proposed parametrization can be employed. The modular formulation allows to extend this framework to controller synthesis with different performance criteria, input-output settings, and various system properties. Finally, we validate the proposed approach through a numerical example.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A posteriori learning for quasi-geostrophic turbulence parametrization
The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that …
Generative convective parametrization of dry atmospheric boundary layer
Turbulence parametrizations will remain a necessary building block in kilometer-scale Earth system models. In convective boundary layers, where the mean vertical gradients of conserved properties such as potential temper…
Generative Adversarial NetworkData-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics
In this paper, we present a data-driven controller design method for continuous-time nonlinear systems, using no model knowledge but only measured data affected by noise. While most existing approaches focus on systems w…
Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
This paper characterizes a new parametrization of nonlinear networked incrementally $L_2$-bounded operators in discrete time. The distinctive novelty is that our parametrization is \emph{free} -- that is, a sparse large-…
LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins
This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LL…
Decision MakingLanguage ModelingLanguage ModellingLarge Language Model+1