Adaptive neuro-fuzzy modeling of a soft finger-like actuator for cyber-physical industrial systems

Soft robotics is a trending area of research that can revolutionize the use of robotics in industry 4.0 and cyber-physical systems including intelligent industrial systems and their interactions with the human. These robots have notable adaptability to objects and can facilitate many tasks in everyday life. One potential use of these robots is in medical applications. Due to the soft body of these robots, they are a suitable replacement for applications like rehabilitation and exoskeletons. In this paper, we present the neuro-fuzzy modeling of a soft pneumatic finger-like actuator. This actuator is a fiber-reinforced soft robot with the shape and dimensions of a real finger and moves in planar motion. A bending sensor is used as a feedback for curvature motion of this actuator. In order to model this actuator, an adaptive neuro-fuzzy inference system is utilized to overcome the hardship in the modeling of the nonlinear performance of the soft materials. An experimental setup is designed to obtain suitable input-output data needed for modeling. The results show the applicability of the utilized method in the modeling of the soft actuator.