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

2025-09-16

AI Summary

To address the critical need for precise intracranial pressure (ICP) waveform regulation in cerebrospinal fluid dynamics and neuropathology research, this paper proposes a two-layer adaptive learning control framework integrating model predictive control (MPC), disturbance observers, and Bayesian optimization for soft robotic actuators. The framework enables online learning of reference trajectories and safe closed-loop regulation under unknown nonlinear system dynamics. In vitro brain phantom experiments demonstrate that, compared to a PID controller, the proposed method reduces mean and maximum motor position tracking errors by 83% and 73%, respectively. Furthermore, Bayesian optimization achieves target ICP waveforms—satisfying specified mean and amplitude constraints—in only 20 iterations, significantly improving both waveform fidelity and convergence speed.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Modulating intracranial pressure waveforms using soft robotics
• Learning nonlinear ICP dependence on motor position online
• Achieving safe and precise motor position tracking under constraints

Innovation

• Model Predictive Control with disturbance observer
• Bayesian optimization for online learning
• Two-layer framework for safe modulation