Controlling soft robots with precision is a challenge due to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman operator theory offers a way to construct explicit dynamical models of soft robots and to control them using established model-based control methods. This approach is data driven, yet yields an explicit control-oriented model rather than just a “black-box” input–output mapping. This work describes a Koopman-based system identification method and its application to model predictive control (MPC) design for soft robots. Three MPC controllers are developed for a pneumatic soft robot arm via the Koopman-based approach, and their performances are evaluated with respect to several real-world trajectory following tasks. In terms of average tracking error, these Koopman-based controllers are more than three times more accurate than a benchmark MPC controller based on a linear state-space model of the same system, demonstrating the utility of the Koopman approach in controlling real soft robots.

中文翻译：

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使用 Koopman 算子理论对软机器人进行数据驱动控制

由于难以构建适合基于模型的控制设计技术的模型，因此精确控制软机器人是一项挑战。Koopman 算子理论提供了一种构建软机器人的显式动力学模型并使用已建立的基于模型的控制方法来控制它们的方法。这种方法是数据驱动的，但产生了一个明确的面向控制的模型，而不仅仅是一个“黑盒”输入-输出映射。这项工作描述了一种基于 Koopman 的系统识别方法及其在软机器人模型预测控制 (MPC) 设计中的应用。通过基于 Koopman 的方法为气动软机器人手臂开发了三个 MPC 控制器，并针对几个真实世界的轨迹跟踪任务评估了它们的性能。在平均跟踪误差方面，