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.Comment: 8 pages, 9 figures, 4 table