  In this work, we address the inverse kinetics problem of motion planning of
soft biomimetic actuators driven by three chambers. Soft biomimetic actuators
have been applied in many applications owing to their intrinsic softness.
Although a mathematical model can be derived to describe the inverse dynamics
of this actuator, it is still not accurate to capture the nonlinearity and
uncertainty of the material and the system. Besides, such a complex model is
time-consuming, so it is not easy to apply in the real-time control unit.
Therefore, developing a model-free approach in this area could be a new idea.
To overcome these intrinsic problems, we propose a back-propagation (BP) neural
network learning the inverse kinetics of the soft biomimetic actuator moving in
three-dimensional space. After training with sample data, the BP neural network
model can represent the relation between the manipulator tip position and the
pressure applied to the chambers. The proposed algorithm is more precise than
the analytical model. The results show that a desired terminal position can be
achieved with a degree of accuracy of 2.46% relative average error with respect
to the total actuator length.
