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.

翻译：软体机器人在控制方面存在困难，需要采用新颖策略来有效操纵其柔性结构。基于模型的方法因高维度和迟滞效应等非线性特性而面临挑战。相比之下，学习型方法仅基于测量数据即可为不同软体机器人提供非线性模型。本文采用循环神经网络（RNN）预测具有五个自由度（DoF）的关节式软体机器人（ASR）的行为。基于门控循环单元（GRU）的RNN与更常用的长短期记忆（LSTM）网络进行比较，显示出更高的预测精度。循环结构能够捕捉软体机器人因粘弹性或摩擦固有的迟滞效应，这是简单前馈网络无法实现的。该数据驱动模型被应用于非线性模型预测控制（NMPC）中，重点关注RNN隐藏状态的正确处理。本文提出一种训练方法，允许在每个控制周期中利用测量值。这使得基于传感器数据的短时域精确预测成为可能，这对闭环NMPC至关重要。所提出的学习型NMPC在气动五自由度ASR实验中实现了平均1.2度的轨迹跟踪误差。