Back to Search
Start Over
Attitude control of 3D soft pneumatic actuators based on BP neural network.
- Source :
- Journal of Applied Physics; 6/21/2023, Vol. 133 Issue 23, p1-8, 8p
- Publication Year :
- 2023
-
Abstract
- 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° and 1.2°, 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- PNEUMATIC actuators
SOFT robotics
SOFT errors
PNEUMATIC control
ROBOTICS
Subjects
Details
- Language :
- English
- ISSN :
- 00218979
- Volume :
- 133
- Issue :
- 23
- Database :
- Complementary Index
- Journal :
- Journal of Applied Physics
- Publication Type :
- Academic Journal
- Accession number :
- 164435003
- Full Text :
- https://doi.org/10.1063/5.0153712