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Koopman Operators for Modeling and Control of Soft Robotics

2023-01-23 20:28:59
Lu Shi, Zhichao Liu, Konstantinos Karydis

Abstract

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.

Abstract (translated)

目的审查:我们回顾了利用克oopman operator理论建模和控制软机器人的最新进展。最新发现:我们在该领域最近的研究工作中发现了以下趋势。(1) 在数据驱动近似克oopman operator时使用的提升函数的设计对于软机器人非常重要。(2) 强调了可靠性考虑。建议进行工作以在建模和控制过程中减少不确定性和噪声的影响。(3) 克oopman operator已经被嵌入到不同的模型控制结构中,以驱动软机器人。总结:由于它们的 compliance 和非线性,软机器人的建模和控制面临关键挑战。为了解决这些问题,我们提出了克oopman operator based approaches,试图以线性方式表达非线性系统。克oopman operator 能够全球线性化,减少非线性度和/或作为软机器人模型约束的模型限制。在软机器人系统中的各种实现都在审查中得到了演示和总结。

URL

https://arxiv.org/abs/2301.09708

PDF

https://arxiv.org/pdf/2301.09708.pdf


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