Feedforward Pressure-Regulated Position Control of Soft Ballooning Actuators Using a Modified Prandtl�Ishlinskii Model
Nashil Sowaruth, Nicolas Herzig
AI summary
Problem
Precise position control of soft ballooning membrane actuators is hindered by strong, path-dependent hysteresis that degrades simple feedforward pressure regulation.
Approach
The authors develop and implement two data-driven feedforward controllers—a baseline polynomial fit and a hysteresis-aware Modified Prandtl-Ishlinskii (MPI) inverse model—on an embedded platform to compute required pressure from target displacement.
Key results
- MPI controller improves overall position accuracy by 71% over polynomial baseline during full inflation
- Achieves a mean tracking error of 0.685 mm (9.8% of range) during partial inflation-deflation cycles
- Validates real-time numerical inversion of the MPI model on an Arduino for embedded feedforward control
- Demonstrates that hysteresis-aware modeling is essential for accurate pressure-regulated position control in soft actuators
Why it matters
Enables more reliable and precise control of soft pneumatic actuators for medical and adaptive robotic applications where hysteresis is a major limiting factor.
Abstract
Thanks to their compliance and adaptability, soft actuators are promising devices for medical applications and the exploration of unstructured environment. However, their nonlinear behaviour, including strong hysteresis effects, presents challenges for accurate position control. This work investigates two data-driven feedforward control strategies for controlling the position of a Hyper-Elastic Ballooning Membrane Actuator (HBMA): a baseline single polynomial fit model and a hysteresis-aware Modified Prandtl-Ishlinskii (MPI) model. Comparative experiments demonstrate that hysteresis- aware control substantially improves accuracy. Specifically, incorporating hysteresis improved overall accuracy by 71% when the HBMA was inflated up to 20.5 mm. During partial inflation-deflation cycles, the MPI controller achieved a mean error of 0.685 mm, corresponding to 9.8% of the 7 mm displacement range. These results highlight the limitations of using feedforward control alone in soft robotic actuation while emphasising the benefits of hysteresis-aware modelling. The findings contribute to the ongoing effort to develop effective control strategies for soft robotic systems.