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arXiv · 2509.13109

Model Predictive Control with Reference Learning for Soft Robotic Intracranial Pressure Waveform Modulation

Fabian Flürenbrock·Yanick Büchel·Johannes Köhler·Marianne Schmid Daners·Melanie N. Zeilinger
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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.

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Fabian Flürenbrock, Yanick Büchel, Johannes Köhler, Marianne Schmid Daners, Melanie N. Zeilinger. 2025-09-16. Model Predictive Control with Reference Learning for Soft Robotic Intracranial Pressure Waveform Modulation. https://arxiv.org/abs/2509.13109

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