Model Predictive Control with Reference Learning for Soft Robotic Intracranial Pressure Waveform Modulation
Abstract
This paper introduces a learning-based control framework for a soft robotic actuator system designed to modulate intracranial pressure (ICP) waveforms, which is essential for studying cerebrospinal fluid dynamics and pathological processes underlying neurological disorders. A two-layer framework is proposed to safely achieve a desired ICP waveform modulation. First, a model predictive controller (MPC) with a disturbance observer is used for offset-free tracking of the system's motor position reference trajectory under safety constraints. Second, to address the unknown nonlinear dependence of ICP on the motor position, we employ a Bayesian optimization (BO) algorithm used for online learning of a motor position reference trajectory that yields the desired ICP modulation. The framework is experimentally validated using a test bench with a brain phantom that replicates realistic ICP dynamics in vitro. Compared to a previously employed proportional-integral-derivative controller, the MPC reduces mean and maximum motor position reference tracking errors by 83 % and 73 %, respectively. In less than 20 iterations, the BO algorithm learns a motor position reference trajectory that yields an ICP waveform with the desired mean and amplitude.
Cite
@article{arxiv.2509.13109,
title = {Model Predictive Control with Reference Learning for Soft Robotic Intracranial Pressure Waveform Modulation},
author = {Fabian Flürenbrock and Yanick Büchel and Johannes Köhler and Marianne Schmid Daners and Melanie N. Zeilinger},
journal= {arXiv preprint arXiv:2509.13109},
year = {2026}
}