Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems For Control

Madhur Tiwari; George Nehma; Bethany Lusch

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arXiv · 2309.04074

Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems For Control

Madhur Tiwari·George Nehma·Bethany Lusch

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Abstract

This work focuses on developing a data-driven framework using Koopman operator theory for system identification and linearization of nonlinear systems for control. Our proposed method presents a deep learning framework with recursive learning. The resulting linear system is controlled using a linear quadratic control. An illustrative example using a pendulum system is presented with simulations on noisy data. We show that our proposed method is trained more efficiently and is more accurate than an autoencoder baseline.

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BibTeX RIS

Madhur Tiwari, George Nehma, Bethany Lusch. 2023-09-08. Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems For Control. https://arxiv.org/abs/2309.04074

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