{"ID":2828885,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.13170","arxiv_id":"2512.13170","title":"Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks","abstract":"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.","short_abstract":"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 ta...","url_abs":"https://arxiv.org/abs/2512.13170","url_pdf":"https://arxiv.org/pdf/2512.13170v2","authors":"[\"Deepak Ingole\",\"Valentin Bhend\",\"Shiva Ganesh Murali\",\"Oliver Dobrich\",\"Alisa Rupenyan\"]","published":"2025-12-15T10:30:40Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.LG\",\"eess.SY\",\"math.OC\"]","methods":"[]","has_code":false}