用深度柯尔莫哥洛夫模型实现软体机器人高精度控制
Multi-segment Soft Robot Control via Deep Koopman-based Model Predictive Control
- 通过深度学习近似柯尔莫哥洛夫算子，将非线性动力学线性化
- 在模型预测控制中优化输入，实现轨迹跟踪误差最小化
- 适用于多段软体机器人，实测验证控制精度高
与传统刚性机器人相比，多段软体机器人因采用柔性材料具备灵活性和顺应性，在环境中可实现安全交互与灵巧操作。然而，其高维、非线性、时变及无限自由度特性使得精确动态控制（如轨迹跟踪与位姿到达）面临挑战。为此，本文提出基于深度柯尔莫哥洛夫的模型预测控制框架（DK-MPC），首先利用采样数据的深度学习方法近似柯尔莫哥洛夫算子，将软体机器人的高维非线性动力学转化为有限维线性表示；其次，将该线性模型嵌入模型预测控制框架，计算使期望状态轨迹与实际轨迹间跟踪误差最小的最优控制输入。在真实软体机器人Chordata上的实验表明，DK-MPC能够实现高精度控制，展现出在软体机器人未来应用中的潜力。
原文摘要 · Abstract (English)
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.
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