标题： 多段软机器人控制的深度Koopman模型预测控制 显示英文标题

标题： Multi-segment Soft Robot Control via Deep Koopman-based Model Predictive Control

摘要： 软体机器人与传统刚性机器人相比，由于其多个部分使用软材料带来了灵活性和顺应性，在环境中的安全交互和灵巧操作具有优势。然而，由于其高维性、非线性、时变特性以及无限自由度的特点，实现精确动态控制（如轨迹跟踪和位置到达）一直是个挑战。为了解决这些挑战，我们提出了一种基于深度Koopman模型预测控制（DK-MPC）的框架来处理多段软体机器人。 我们首先采用深度学习方法结合采样数据来逼近Koopman算子，从而将软体机器人的高维非线性动力学线性化为有限维的线性表示。 其次，在模型预测控制框架内利用这个线性化模型来计算最优控制输入，以最小化期望状态轨迹与实际状态轨迹之间的跟踪误差。 对软体机器人“Chordata”进行的真实世界实验表明，DK-MPC能够实现高精度控制，展示了DK-MPC在未来软体机器人应用中的潜力。 显示英文摘要

摘要： Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot "Chordata" demonstrate that DK-MPC could achieve high-precision control, showing the potential of DK-MPC for future applications to soft robots.