Electromagnetic Navigation Systems (eMNS) have gained considerable attention for minimally invasive surgery and targeted drug delivery. While most of the literature relies on quasi-static control of these systems, recent work has demonstrated the benefits of dynamic approaches. However, trajectory tracking far from equilibrium states remains largely unaddressed. We close this gap by demonstrating the first swing-up of a magnetically actuated inverted pendulum using the clinically-ready Navion eMNS. Although the inverted pendulum is not clinically relevant in itself, the proposed method utilizes torques and forces as control objectives, making it applicable to other magnetically actuated devices such as catheters and guidewires. Our approach combines trajectory optimization that accounts for internal eMNS dynamics with time-varying Linear Quadratic Regulator (LQR) state feedback and Iterative Learning Control (ILC), which leverages previous trial data and the system's dynamic model to progressively refine the feedforward command. While LQR alone fails due to the complex phenomena of magnetic actuation, ILC enables successful swing-up within six iterations. Furthermore, post-experimental analysis reveals that the learned ILC correction closely matches the torque discrepancy predicted by high-fidelity magnetic field model calibration, suggesting learning and adaptation as a promising tool to deal with uncertainties in electromagnetic actuation arising, e.g., from patient-specific physiological motion patterns and field model calibration inaccuracies.

翻译：电磁导航系统（eMNS）在微创手术和靶向药物递送领域引起了广泛关注。尽管现有文献大多依赖这些系统的准静态控制，但近期研究已展示了动态方法的优势。然而，远离平衡状态的轨迹跟踪问题仍未得到充分解决。我们通过使用临床级Navion eMNS首次实现磁驱动倒立摆的摆动上升，弥补了这一空白。尽管倒立摆本身不具备临床相关性，但所提出的方法以扭矩和力作为控制目标，使其可适用于其他磁驱动装置，如导管和导丝。我们的方法将考虑eMNS内部动力学的轨迹优化与时变线性二次型调节器（LQR）状态反馈及迭代学习控制（ILC）相结合，其中ILC利用先前的试验数据与系统动力学模型逐步优化前馈指令。由于磁驱动现象的复杂性，仅使用LQR无法成功，而ILC可在六次迭代内实现摆动上升。此外，实验后分析表明，学习到的ILC校正与高保真磁场模型标定所预测的扭矩偏差高度吻合，这表明学习与自适应方法有望应对电磁驱动中的不确定性，例如患者特定生理运动模式与磁场模型标定误差所引入的不确定性。