用RNN建模软机器人，实现高精度轨迹跟踪控制。
Learning-based Nonlinear Model Predictive Control of Articulated Soft Robots using Recurrent Neural Networks
- 用GRU网络捕捉软体机器人的迟滞特性，优于传统LSTM。
- 闭环控制下实现平均1.2度的轨迹跟踪误差。
- 适合需要精准控制柔顺结构的机器人研究者。
软体机器人因其柔性结构在控制上面临挑战，传统模型方法受限于高维度和非线性（如迟滞效应）。本文采用基于门控循环单元（GRU）的循环神经网络（RNN）对五自由度（DoF）关节式软体机器人（ASR）进行行为预测，相较常用的长短期记忆（LSTM）网络表现出更高精度。循环结构可有效建模由粘弹性或摩擦引起的迟滞效应，而前馈网络无法捕捉此类动态。将数据驱动的RNN嵌入非线性模型预测控制（NMPC），并提出一种训练方法，使传感器测量值可在每个控制周期中被利用。该方法实现了基于传感器数据的短时程精准预测，对闭环控制至关重要。实验表明，所提学习型NMPC在气动五自由度ASR上实现了平均1.2°的轨迹跟踪误差。
原文摘要 · Abstract (English)
Soft robots pose difficulties in terms of control, requiring novel strategies to effectively manipulate their compliant structures. Model-based approaches face challenges due to the high dimensionality and nonlinearities such as hysteresis effects. In contrast, learning-based approaches provide nonlinear models of different soft robots based only on measured data. In this paper, recurrent neural networks (RNNs) predict the behavior of an articulated soft robot (ASR) with five degrees of freedom (DoF). RNNs based on gated recurrent units (GRUs) are compared to the more commonly used long short-term memory (LSTM) networks and show better accuracy. The recurrence enables the capture of hysteresis effects that are inherent in soft robots due to viscoelasticity or friction but cannot be captured by simple feedforward networks. The data-driven model is used within a nonlinear model predictive control (NMPC), whereby the correct handling of the RNN's hidden states is focused. A training approach is presented that allows measured values to be utilized in each control cycle. This enables accurate predictions of short horizons based on sensor data, which is crucial for closed-loop NMPC. The proposed learning-based NMPC enables trajectory tracking with an average error of 1.2deg in experiments with the pneumatic five-DoF ASR.
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