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.

翻译：本文提出一种基于学习的控制框架，用于设计调制颅内压力波形的软体机器人执行器系统，该系统对研究脑脊液动力学及神经系统疾病潜在病理过程至关重要。我们提出一种双层框架以安全实现期望的ICP波形调制。首先，采用配备扰动观测器的模型预测控制器，在安全约束下实现系统电机位置参考轨迹的无静差跟踪。其次，为解决ICP对电机位置未知的非线性依赖关系，我们采用贝叶斯优化算法在线学习能产生期望ICP调制的电机位置参考轨迹。该框架通过配备脑部仿生体（可体外复现真实ICP动力学特性）的测试平台进行了实验验证。与先前采用的比例-积分-微分控制器相比，MPC将电机位置参考跟踪的平均误差和最大误差分别降低了83%和73%。BO算法在不足20次迭代中即可学习到能产生具有期望平均值与幅度的ICP波形的电机位置参考轨迹。