Iterative Learning Control Systems

Últimos artículos

• Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs

  Kairun Zhang, Haoyu Li, Yanjun Zhao, Yifan Sun, Huan Zhang · 1 de junio de 2026

  Zeroth-order optimizers have recently emerged as an attractive approach for fine-tuning large language models (LLMs), as they avoid backpropagation and can substantially reduce memory overhead relative to standard first-order training. However, existing zeroth-order methods rely on hand-crafted, sta…

• Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction

  Chengzhi Xu, Yuyang Wang, Lai Wei, Lichao Sun, Weiran Huang · 18 de marzo de 2026

  Recently, multimodal large language models (MLLMs) have attracted increasing research attention due to their powerful visual understanding capabilities. While they have achieved impressive results on various vision tasks, their performance on chart-to-code generation remains suboptimal. This task re…

• Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control

  Runze Lin, Ziqi Zhuo, Junghui Chen, Lei Xie, Hongye Su · 17 de marzo de 2026

  A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions generated during exploration-exploitation, which poses substantial safety risks during both training and deployment. In industrial process control, the lack of formal stability and convergence guar…

• Anytime-Valid Answer Sufficiency Certificates for LLM Generation via Sequential Information Lift

  Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma · 6 de enero de 2026

  We introduce Sequential-EDFL (Empirical Dynamic Formal Lift), which applies anytime-valid sequential testing to language model generation stopping. Our approach tracks information lift, defined as the log-likelihood ratio between the full model and deliberately weakened "skeleton" baselines, using s…

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

  Deepak Ingole, Valentin Bhend, Shiva Ganesh Murali, Oliver Dobrich, Alisa Rupenayan · 16 de diciembre de 2025

  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 …