Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to fabricate replaceable components, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 0.040 and 0.030 compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future.

翻译：软体机器人凭借其柔软性、灵活性和安全性，已广泛应用于手术、康复和仿生学等领域。然而，由于软材料的复杂性，即使采用相同的模具和制造工艺，也难以制造出两个完全相同的软体机器人。同时，系统的广泛应用需要具备制造可替换组件的能力，即互换性。鉴于这一特性的必要性，本文从控制方法角度引入了一种混合自适应控制器以实现互换性。该方法利用离线训练的循环神经网络控制器来处理软体机器人的非线性及延迟响应，并通过在线优化运动学控制器来减小上述神经网络控制器产生的误差。验证实验采用了基于相同模具但具有不同形变特性的软体气动机器人。实验结果表明，在不同驱动配置和不同机器人系统中，系统均能跟踪期望轨迹，其误差分别仅为工作空间长度的0.040和0.030。该自适应控制器在不同控制频率和期望速度下也表现出良好性能。该控制器赋予了软体机器人广泛应用的潜力，未来工作可包括不同形式的离线和在线控制器，并可提出权重参数调整策略。