Koopman Operators for Modeling and Control of Soft Robotics
Koopman Operators for Modeling and Control of Soft Robotics
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DOI：
10.1007/s43154-023-00099-8
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Lu Shi;Zhichao Liu;Konstantinos Karydis
中科院分区：
文献类型：
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作者：
Lu Shi;Zhichao Liu;Konstantinos Karydis
审查目的我们回顾了利用库普曼算子理论对软机器人进行建模和控制的算法开发和验证的最新进展。最新发现我们确定了该领域近期研究工作的以下趋势。 (1)库普曼算子的数据驱动近似中使用的提升函数的设计对于软机器人至关重要。 (2)强调稳健性考虑。提出了减少建模和控制过程中不确定性和噪声影响的工作。 (3)库普曼算子已被嵌入到不同的基于模型的控制结构中来驱动软机器人。总结由于软机器人的柔顺性和非线性，软机器人的建模和控制面临着关键挑战。为了解决这些挑战，提出了基于库普曼算子的方法，试图以线性方式表达非线性系统。库普曼算子支持全局线性化以减少非线性和/或用作软机器人基于模型的控制算法中的模型约束。综述中对软机器人系统的各种实现进行了说明和总结。
Purpose of ReviewWe review recent advances in algorithmic development and validation for modeling and control of soft robots leveraging the Koopman operator theory.Recent FindingsWe 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.SummaryBecause 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.