Accurate Pouring using Model Predictive Control Enabled by Recurrent Neural Network
Accurate Pouring using Model Predictive Control Enabled by Recurrent Neural Network
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DOI：
10.1109/iros40897.2019.8967802
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Tianze Chen;Yongqiang Huang;Yu Sun
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文献类型：
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作者：
Tianze Chen;Yongqiang Huang;Yu Sun
人类经常执行倾倒任务，并且无论液体的复杂动力学如何，都表现出一致的准确性。模型预测控制（MPC）似乎是一个自然的候选解决方案，考虑到其在工业应用中的广泛使用的任务，精确浇注。然而，MPC需要所讨论的系统的模型。由于液体动力学的精确模型难以获得，MPC用于浇注任务的有用性是不确定的。在这项工作中，我们使用递归神经网络（RNN）模拟水的动态，这使得使用MPC进行浇注控制。我们使用我们自己制作的物理系统评估了我们的RNN支持的MPC控制器，平均浇注误差为16。4 mL，5个不同来源的容器。我们还比较了我们的控制器与基线开关控制器，并表明，我们的控制器实现了更高的精度比基线控制器。
Humans perform the task of pouring often and in which exhibit consistent accuracy regardless of the complicated dynamics of the liquid. Model predictive control (MPC) appears to be a natural candidate solution for the task of accurate pouring considering its wide use in industrial applications. However, MPC requires the model of the system in question. Since an accurate model of the liquid dynamics is difficult to obtain, the usefulness of MPC for the pouring task is uncertain. In this work, we model the dynamics of water using a recurrent neural network (RNN), which enables the use of MPC for pouring control. We evaluated our RNN-enabled MPC controller using a physical system we made ourselves and averaged a pouring error of 16. 4mL over 5 different source containers. We also compared our controller with a baseline switch controller and showed that our controller achieved a much higher accuracy than the baseline controller.