# Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks

arXiv: https://arxiv.org/abs/2512.13170 · HTML: https://mlsanity.com/paper/2512.13170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization](https://mlsanity.com/paper/2103.15416/) (2021)
- [Differentiable Collision Avoidance Using Collision Primitives](https://mlsanity.com/paper/2204.09352/) (2022)
- [Learning-Based Repetitive Precision Motion Control with Mismatch Compensation](https://mlsanity.com/paper/2111.10246/) (2021)
- [Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin](https://mlsanity.com/paper/2402.16645/) (2024)
- [The Dynamic Model of the UR10 Robot and its ROS2 Integration](https://mlsanity.com/paper/2502.11940/) (2025)

## Abstract (arXiv)

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.