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 replace inner components without highly affecting system performance, 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 3.3 +- 2.9% and 4.3 +- 4.1% compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller is also compared with a model-based controller in simulation. 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：软体机器人因其柔软性、灵活性和安全性，在手术、康复和仿生学等众多领域得到了广泛应用。然而，由于软体材料的复杂性，即使使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，系统的广泛使用要求能够在基本不影响系统性能的前提下更换内部组件，这即为互换性。鉴于这一特性的必要性，本文从控制方法的角度引入了一种混合自适应控制器来实现互换性。该方法利用离线训练的递归神经网络控制器来处理软体机器人的非线性和延迟响应，并进一步应用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。实验中包含了采用相同模具但具有不同变形特性的软气动机器人。实验中，具有不同驱动配置的系统以及不同的机器人跟随期望轨迹的误差分别相对于工作空间长度为3.3±2.9%和4.3±4.1%。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的性能。此外，本文还将该控制器与仿真中的基于模型的控制器进行了比较。该控制器赋予了软体机器人广泛应用潜力，未来的工作可能包括不同的离线与在线控制器，也可能提出一种权重参数调整策略。