A computationally efficient dynamical model of fluidic soft actuators and its experimental verification. (April 2019)

• Record Type: Journal Article
• Authors: Wang, Tao; Zhang, Yunce; Zhu, Yipeng; Zhu, Shiqiang
• Is Part Of: Mechatronics. Volume 58(2019)
• Page Start: 1
• Page End: 8
• Publication Date: 2019-04
• Subjects: Dynamical model -- Bending actuators -- Computationally efficient -- Fluidic power -- Soft robotics; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2018.11.012
• Languages: English
• ISSNs: 0957-4158

Abstract

Abstract: This work develops a computationally efficient dynamical model for fluidic bending soft actuators in consideration of distributed-mass effect. The model in universal form is based on the Lagrangian approach and the constant curvature assumption. Since the model parameters are dependent on actuator type, a case study on the soft actuator with fiber-reinforced structure is carried out due to its convenience in manufacture and resistance to trivial deformation. The expressions of strain energy, gravitational potential energy, kinetic energy and generalized force are derived with respect to a generalized coordinate defined by the bending angle of the soft actuator. Material deformation and fluidic volume variation are also considered and derived in the model. Additionally, Taylor series expansions are employed to reduce the model complexity by neglecting the high-order terms reasonably. Finally, an experimental setup for the soft actuator is built and a curvature sensor is utilized to detect the bending angle. Experiments under typical operating conditions are implemented to verify the model and the results show that the measured data are in good accordance with the predicted responses. The proposed dynamical model can serve as a basis for further model-based control algorithm design for fluidic soft actuators.