Online Estimation of the Koopman Operator Using Fourier Features

Salam, Tahiya; Li, Alice Kate; Ani, Hsieh, M.

doi:10.48550/arxiv.2212.01503

Cited by 1 publication

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“…Other than machine learning approaches, Koopman spectral theory [59] has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on the space of all possible measurement functions of the system [60]. Recent research efforts have sought to use the Koopman operator in robotics.…”

Section: Introductionmentioning

confidence: 99%

Koopman Operators for Modeling and Control of Soft Robotics

Shi¹,

Liu²,

Karydis³

2023

Preprint

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Purpose of review: We review recent advances in algorithmic development and validation for modeling and control of soft robots leveraging the Koopman operator theory. Recent findings: We identify the following trends in recent research efforts in this area. (1) The design of lifting functions used in the data-driven approximation of the Koopman operator is critical for soft robots. (2) Robustness considerations are emphasized. Works are proposed to reduce the effect of uncertainty and noise during the process of modeling and control. (3) The Koopman operator has been embedded into different model-based control structures to drive the soft robots.Summary: Because of their compliance and nonlinearities, modeling and control of soft robots face key challenges. To resolve these challenges, Koopman operator-based approaches have been proposed, in an effort to express the nonlinear system in a linear manner. The Koopman operator enables global linearization to reduce nonlinearities and/or serves as model constraints in model-based control algorithms for soft robots. Various implementations in soft robotic systems are illustrated and summarized in the review.

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Section: Introductionmentioning

confidence: 99%