A Neural Network Based Dynamic Control Method for Soft Pneumatic Actuator with Symmetrical Chambers

• Main Authors: Yiqing Li, Yan Cao, Feng Jia
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Actuators
• Subjects: soft pneumatic actuator; symmetrical structure; dynamic modeling; model predictive control
• Online Access: https://www.mdpi.com/2076-0825/10/6/112

Dynamic modeling and control of the soft pneumatic actuators are challenging research. In this paper, a neural network based dynamic control method used for a soft pneumatic actuator with symmetrical chambers is proposed. The neural network is introduced to create the dynamic model for predicting the state of the actuator. In this dynamic model, the effect of the uninflated rubber block on bending deformation is considered. Both pressures of the actuator are used for predicting the state of the actuator during the bending motion. The controller is designed based on this dynamic model for trajectory tracking control. Three types of trajectory tracking control experiments are performed to validate the proposed method. The results show that the proposed control method can control the motion of the actuator and track the trajectory effectively.