用学习控制调节脑压波形，实现精准安全的神经压力调控。
Model Predictive Control with Reference Learning for Soft Robotic Intracranial Pressure Waveform Modulation
- 分层控制：先用模型预测控制追踪位置，再用贝叶斯优化在线学出目标波形对应的位置轨迹。
- 相比传统PID，位置跟踪误差平均降低83%，最大误差降低73%。
- 适合神经工程与智能医疗设备研究者，用于脑压动态模拟与治疗探索。
本文提出一种基于学习的控制框架，用于软体机器人执行器系统对颅内压（ICP）波形的调控，以研究脑脊液动力学及神经疾病病理机制。采用两层结构：首先，基于带有扰动观测器的模型预测控制（MPC），在安全约束下实现电机位置参考轨迹的无偏跟踪；其次，为解决ICP与电机位置间未知的非线性关系，采用贝叶斯优化（BO）算法在线学习生成期望ICP调制所需的位置参考轨迹。该框架在含脑假体的实验平台上进行验证，可再现真实的体外颅内压动态。相比先前使用的比例-积分-微分（PID）控制器，所提MPC使电机位置参考轨迹的平均跟踪误差和最大误差分别降低83%和73%。在不到20次迭代内，BO算法成功学习到能产生期望均值与振幅的ICP波形所需的位置轨迹。
原文摘要 · Abstract (English)
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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