Attitude control of 3D soft pneumatic actuators based on BP neural network

Soft pneumatic robotics have attracted considerable attention in recent years due to their deformation capabilities, which far exceed those of conventional robotics. However, precise control of soft pneumatic actuators remains a challenge due to the lack of model-based control techniques. This work aims to employ a high-precision and low-cost backpropagation (BP) neural network-based model method to control a 3D soft pneumatic actuator. Experiments show that this BP neural network-based model control method performs well in terms of precision, in which the errors of bending angle and deflection angle are within 0.8 degrees and 1.2 degrees, respectively, and the end point position error of the soft actuator is less than 2.5 mm, which is significantly better than traditional modeling methods, demonstrating the application potential of soft robots for high-precision operations.