通过迭代学习自动调优非线性预测控制参数，实现机器人制造任务的在线自适应优化。
Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks
- 基于任务性能反馈，用经验敏感矩阵迭代调整NMPC权重，无需梯度计算。
- 仅4次在线重复即达到接近贝叶斯优化100次离线调优的跟踪精度（RMSE误差<0.3%）。
- 适合需要持续优化的重复性工业机器人任务，如碳纤维缠绕等高精度制造场景。
制造过程常受环境漂移和系统磨损影响，即使在重复操作中也需要重新调参。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）权重矩阵自动调优的迭代学习框架。受范数最优迭代学习控制（ILC）启发，该方法在任务重复中自适应调整NMPC权重Q和R，以最小化与跟踪精度、控制努力及饱和相关的关键绩效指标（KPI）。不同于需对NMPC求解器进行反向传播的梯度方法，我们构建了经验敏感矩阵，实现无需解析导数的结构化权重更新。在UR10e机器人执行四面体芯体碳纤维缠绕的仿真中验证，该方法仅需4次在线重复即可收敛至接近离线贝叶斯优化（BO）的结果（RMSE误差低于0.3%），而后者需100次离线评估。该方法为重复性机器人任务提供了兼具高精度与在线适应性的实用调优方案。
原文摘要 · Abstract (English)
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.
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