Dynamic characterization and simulation of two-link soft robot arm with pneumatic muscles. (September 2016)

• Record Type: Journal Article
• Authors: Hošovský, A.; Piteľ, J.; Židek, K.; Tóthová, M.; Sárosi, J.; Cveticanin, L.
• Is Part Of: Mechanism and machine theory. Volume 103(2016:Sep.)
• Page Start: 98
• Page End: 116
• Publication Date: 2016-09
• Subjects: Pneumatic artificial muscle -- Planar arm -- Robot dynamics; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2016.04.013
• Languages: English
• ISSNs: 0094-114X

Abstract

Abstract: Pneumatic artificial muscles (PAMs) belong to the group of nonconventional actuators with remarkable force/weight ratio that can be used for the construction of soft mechanisms safe in contact with humans. In order to be able to design an effective control of 2-link soft robot arm actuated with PAMs, a dynamic model of this system needs to be derived. We use a PAM dynamic model derived using first principles modeling (for contraction, pressure, and air flow dynamics) and ANFIS-based approximation based on the experimental data for the muscle force function. To derive the dynamics of the robot arm, we use Lagrangian mechanics approach for planar arm with the inertial and mass data based on the 3D CAD model. To validate the complete dynamic model of the soft robot arm, we used a gravity test (without PAM actuation) and pulse excitation for PAM control. The results confirm good validity of the dynamic model for all relevant variables (joint angles, muscle contractions, and pressures) as well as the dynamic coupling between the joints. Highlights: A phenomenological model of PAM is utilized for the derivation of actuator model of the whole system. A highly accurate ANFIS-based approximator of PAM force function is used. A complete dynamic model of two-DOF PAM-based planar arm suitable for model-based design or verification is derived. The results of validation show good correspondence of model responses with real system.