针对跳动组织中神经丝插入的相对运动控制,提升定位精度与稳定性。
Preview-Based Relative-Motion Control of an Insertion Tool for Neural-Thread Placement in Pulsating Tissue

- 在组织相对坐标系中设计控制器,预测组织运动并调节针尖位置。
- 接触时相对定位误差低至1.9微米,比传统方法降低90%以上。
- 适用于柔性神经电极插入场景,适合需高精度控制的研究者。
柔性神经电极需在心脏和呼吸波动的皮层表面精确插入指定深度。传统以实验室固定点为基准的控制器无法区分插入动作与组织运动,导致深度偏移和接触时相对速度误差。本文提出基于预览的相对运动控制:利用谐波观测器预测延迟的皮层运动,在控制时域内构建约束型模型预测控制(MPC),限制执行器用力和横向相对速度,并引入增强扰动状态消除持续接触力与模型偏差带来的稳态偏移。在1-自由度MuJoCo仿真中,控制器达到自由空间12.0微米、接触状态1.9微米的均方根相对定位误差,优于延迟反馈阻抗控制(18.3/176.8微米)和实验室帧PD控制(286.1/275.5微米)。3-自由度扩展使横向剪切速度从1.34降至0.50毫米/秒,横向定位误差2.1微米;软松弛公式在传感退化时仍可求解,而硬约束控制器失效。有限时域增益的双顶点李雅普诺夫证书在反射质量偏差-40%至+50%范围内有效,1-自由度二次规划求解时间低于0.4毫秒(95百分位)。结果为仿真控制基准,非临床安全声明:模型假设针尖为刚性接触点,实际应用前需验证柔性线力学、力约束有效性、生物损伤阈值及硬件感知与延迟特性。
原文摘要 · Abstract (English)
Flexible neural electrode threads must be placed at a prescribed depth while the cortical surface moves with cardiac and respiratory pulsation. A controller tracking a fixed point in the laboratory frame cannot distinguish commanded insertion from tissue motion; the error appears as both a depth offset and relative tip--tissue velocity during contact. This paper formulates thread insertion in tissue-relative coordinates: a harmonic observer predicts delayed cortical-surface motion over the control horizon, a constrained MPC regulates the tip relative to that prediction while limiting actuator effort and lateral relative velocity, and an augmented disturbance state removes the steady offset from persistent contact force and model mismatch. In a 1-DOF MuJoCo benchmark, the controller reaches RMS relative-placement errors of 12.0\um\ free-space and 1.9\um\ in contact, versus 18.3/176.8\um\ for delayed-feedback impedance and 286.1/275.5\um\ for laboratory-frame PD -- the lower contact offset costs more peak contact force (3.43 vs.\ 2.00~mN), since it drives to commanded depth rather than yielding to tissue. A 3-DOF extension reduces lateral shear velocity from 1.34 to 0.50~mm/s at 2.1\um\ lateral placement error, and a feasibility-restoring soft-slack formulation keeps the shear constraint solvable under degraded sensing where a matched hard-constraint controller fails. A two-vertex Lyapunov certificate for the finite-horizon gain holds over $-40\%/{+}50\%$ reflected-mass mismatch, and the 1-DOF QP solves in under 0.4~ms at the 95th percentile. These results are a simulation-based control benchmark, not a clinical safety claim: the modeled tip is a rigid contact point, and flexible-thread mechanics, a validated force constraint, biological damage thresholds, and hardware-realistic sensing and timing remain necessary before deployment.
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