Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This paper presents an iterative learning framework for automatic tuning of Nonlinear Model Predictive Control (NMPC) weighting matrices based on task-level performance feedback. Inspired by norm-optimal Iterative Learning Control (ILC), the proposed method adaptively adjusts NMPC weights Q and R across task repetitions to minimize key performance indicators (KPIs) related to tracking accuracy, control effort, and saturation. Unlike gradient-based approaches that require differentiating through the NMPC solver, we construct an empirical sensitivity matrix, enabling structured weight updates without analytic derivatives. The framework is validated through simulation on a UR10e robot performing carbon fiber winding on a tetrahedral core. Results demonstrate that the proposed approach converges to near-optimal tracking performance (RMSE within 0.3% of offline Bayesian Optimization (BO)) in just 4 online repetitions, compared to 100 offline evaluations required by BO algorithm. The method offers a practical solution for adaptive NMPC tuning in repetitive robotic tasks, combining the precision of carefully optimized controllers with the flexibility of online adaptation.

翻译：制造过程常受环境漂移与系统磨损的扰动，即使在重复性操作中仍需重新调整控制参数。本文提出一种基于任务级性能反馈的非线性模型预测控制（NMPC）权重矩阵自动调参的迭代学习框架。受范数最优迭代学习控制（ILC）启发，所提方法在任务重复执行过程中自适应调整NMPC权重矩阵Q和R，以最小化与跟踪精度、控制能耗及饱和程度相关的关键性能指标（KPIs）。与需要求导NMPC求解器的梯度方法不同，我们构建了经验灵敏度矩阵，从而无需解析导数即可实现结构化权重更新。该框架通过UR10e机器人在四面体芯模上执行碳纤维缠绕的仿真实验得到验证。结果表明：所提方法仅需4次在线迭代即可收敛至接近最优的跟踪性能（均方根误差达离线贝叶斯优化（BO）结果的0.3%以内），而BO算法需要100次离线评估。该方法为重复性机器人任务中的自适应NMPC调参提供了实用解决方案，兼具精细优化控制器的精确性与在线自适应的灵活性。