Amidst Data|Driven Model Reduction and Control

Amidst Data-Driven Model Reduction and Control - IEEE Xplore

In this note, we explore a middle ground between data-driven model reduction and data-driven control.

Amidst Data-Driven Model Reduction and Control - arXiv

Both in model-based and data-based model reduction, the eventual reduced models obtained are used for control typi- cally using classical model- ...

Amidst Data-Driven Model Reduction and Control - ResearchGate

Download Citation | Amidst Data-Driven Model Reduction and Control | In this note, we explore a middle ground between data-driven model reduction and ...

Amidst Data-Driven Model Reduction and Control

We illustrate how the derived family of reduced models can be used for data-driven control of the original system under suitable conditions.

Fast data-driven model reduction for nonlinear dynamical systems

tification and reduced modeling. Model simplicity (or parsimony) is vital for inter- pretability, control, and response prediction for mechan-.

Real-life data-driven model predictive control for building energy ...

Since those models reduce the modeling effort and are relatively easy to utilize, the so-called data-driven MPC recently gained interest, which ...

Data Driven Model Predictive Control for Modular Multilevel ...

This paper proposes a finite control set (FCS) model predictive control (MPC) with reduced computational complexity for modular multilevel converters (MMCs).

Non-intrusive Data-driven Model Reduction for Differential Algebraic ...

It also has the advantage that we can more easily control accuracy of the representations for the original state variables u and ϕ, since they ...

Data-driven model reduction for fast temperature prediction in a ...

The maximum number of racks of the above studies is 28 and the controlling variables are usually <2 or 3 parameters. For the application in real data centers, ...

Fast data-driven model reduction for nonlinear dynamical systems

... reduced modeling. Model simplicity (or parsimony) is vital for interpretability, control, and response prediction for mechanical devices [31].

(PDF) Dynamic data-driven model reduction: adapting reduced ...

PDF | This work presents a data-driven online adaptive model reduction approach for systems that undergo dynamic changes.

Data-Driven Control: The Goal of Balanced Model Reduction

In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly controllable and ...

Data-Driven Model-Based Control Strategies to Improve the Cooling ...

The optimization of HVAC operating conditions, targeting the reduction of building energy use [18] or building electric power [19]. The ...

DATA-DRIVEN CONTROL BASED ON THE BEHAVIORAL ...

quadratic Gaussian (LQG) control, balanced model reduction, and subspace identification. Its success is due to its generality. (it can deal with ...

Data-driven modeling and complexity reduction for nonlinear ...

mechanics, and systems and control, the practice of finding a reduced model is called model reduction. The increase in system complexity and ...

Dynamic data-driven model reduction - Karen E. Willcox

Dynamic data-driven reduced models adapt directly from sensor data to changes in the latent parameters (i.e., external influence), without recourse to the full.

Nonlinear Reduced-Order Modeling from Data by Prof. George Haller.

... model-predictive control of soft robots ... I discuss a recent dynamical-systems-based alternative to machine learning in the data-driven reduced- ...

Data-driven Nonlinear Model Reduction using Koopman Theory

A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of ...

Data-driven modeling and prediction of non-linearizable dynamics ...

Low-dimensional reduced models of high-dimensional nonlinear dynamical systems are critically needed in various branches of applied science and ...

Data-driven model reduction of agent-based systems using ... - PLOS

In this paper, we show how Koopman operator theory can be used to derive reduced models of agent-based systems using only simulation data.