Analytical Modeling and Control of Soft Fast Pneumatic Networks Actuators

The soft fast pneumatic networks actuator (fPNA) featured as large-amplitude motion, and long life span provides a promising solution for varieties of innovative applications, such as the rehabilitation glove, the soft gripper, and the multi-gait robot. However, the infinite freedom in theory impedes its modeling for high-precision control. This paper proposes an analytical model of the fPNA based on the principle of the minimum potential energy. The tight integration of computationally efficiency into the analytical inverse solution of the proposed model enables the model-based control of the fPNA. The validation of the model is experimentally verified by four elaborate fPNAs. Furthermore, an inverse model-based iterative learning controller (ILC) is also constructed for position tracking control of the fPNA.