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 soft actuators remains a challenge the lack model-based techniques. This work aims employ high-precision and low-cost backpropagation (BP) neural network-based model method 3D actuator. Experiments show that this BP performs well terms precision, errors bending angle deflection are within 0.8° 1.2°, respectively, end point position error actuator is less than 2.5 mm, significantly better traditional modeling methods, demonstrating application potential robots for operations.