  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.
