Frontiers in Robotics and AI (Mar 2023)
Adaptive control of a soft pneumatic actuator using experimental characterization data
Abstract
Fiber reinforced soft pneumatic actuators are hard to control due to their non-linear behavior and non-uniformity introduced by the fabrication process. Model-based controllers generally have difficulty compensating non-uniform and non-linear material behaviors, whereas model-free approaches are harder to interpret and tune intuitively. In this study, we present the design, fabrication, characterization, and control of a fiber reinforced soft pneumatic module with an outer diameter size of 12 mm. Specifically, we utilized the characterization data to adaptively control the soft pneumatic actuator. From the measured characterization data, we fitted mapping functions between the actuator input pressures and the actuator space angles. These maps were used to construct the feedforward control signal and tune the feedback controller adaptively depending on the actuator bending configuration. The performance of the proposed control approach is experimentally validated by comparing the measured 2D tip orientation against the reference trajectory. The adaptive controller was able to successfully follow the prescribed trajectory with a mean absolute error of 0.68° for the magnitude of the bending angle and 3.5° for the bending phase around the axial direction. The data-driven control method introduced in this paper may offer a solution to intuitively tune and control soft pneumatic actuators, compensating for their non-uniform and non-linear behavior.
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