Dynamic Mode Decomposition for Real-Time System Estimation of Induction Motor Drives

Gultekin, Muhammed Ali; Zhang, Zhe; Bazzi, Ali M.

doi:10.1109/tia.2022.3231843

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…There are a number of connections between DMDc and other popular system identification methods such as the Eigensystem Realization Algorithm (ERA), the Observer Kalman Identification Method (OKID) and the Koopman operator with control, see [7][8][9]. For some more recent results and applications, we refer the reader to [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach for Implementing Dynamic Mode Decomposition with Control

Nedzhibov

2023

Computation

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Dynamic Mode Decomposition with Control is a powerful technique for analyzing and modeling complex dynamical systems under the influence of external control inputs. In this paper, we propose a novel approach to implement this technique that offers computational advantages over the existing method. The proposed scheme uses singular value decomposition of a lower order matrix and requires fewer matrix multiplications when determining corresponding approximation matrices. Moreover, the matrix of dynamic modes also has a simpler structure than the corresponding matrix in the standard approach. To demonstrate the efficacy of the proposed implementation, we applied it to a diverse set of numerical examples. The algorithm’s flexibility is demonstrated in tests: accurate modeling of ecological systems like Lotka-Volterra, successful control of chaotic behavior in the Lorenz system and efficient handling of large-scale stable linear systems. This showcased its versatility and efficacy across different dynamical systems.

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