arXiv:2512. 23292v5 Announce Type: replace Abstract: The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface.
By Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi, Sajedul Talukder, Diab Abueidda, Seid Koric, Souvik Chakraborty, Syed Bahauddin Alam
The paper proposes a hybrid PID–Deep Reinforcement Learning (DRL) controller for industrial processes, addressing the limitations of traditional PID controllers in complex, non‑linear, multi‑input environments. Using the Industrial Benchmark (IB) to test DRL, the authors develop a multi‑objective reward function and employ a TD3 agent to discover optimal settings for the IB’s ‘Gain’ and ‘Shift’ parameters. These parameters are then fed into a tuned PID controller, yielding a system that combines the optimal performance and efficiency of DRL with the reliability of classical control.
By Zhengyang (Cissy), Gu, Joseph E. Hernandez, John Burtenshaw, Sean Scott, Thomas Cook, Chris Couch
The paper introduces Network Feasibility Geometry Reinforcement Learning (NFG‑RL), a method that enforces multi‑layer network constraints—such as interference, power‑rate coupling, flow conservation, service chains, capacity, latency, and reliability—by transporting a proto‑policy through a differentiable feasibility map. By compiling heterogeneous constraints into typed residual blocks and using a variational transport operator, NFG‑RL ensures almost‑sure feasible execution and shapes exploration and gradients to respect active constraints. Experiments on two wireless‑edge surrogate environments show that NFG‑RL boosts feasible utility by 37.5–41.5 %, cuts raw‑action violations by 48.5–60.8 %, and reduces P99 delay by 57.0–75.5 % compared to leading baselines.
By Zuyuan Zhang, Zeyu Fang, Mahdi Imani, Nathaniel D. Bastian, Tian Lan
arXiv:2606. 00383v1 Announce Type: cross Abstract: While Model Predictive Control (MPC) provides strong stability and robustness, it imposes a significant computational burden on real-time systems.
By Theo Guegan, Dexter Wen Jie Teo
The paper discusses how offline-trained neural networks, when used in closed-loop control systems, can change the state distribution they were trained on, leading to performance degradation. It introduces a taxonomy of failure modes—Closed-Loop Identification Error, deployment-induced Action Error, and continuous Tracking Error—and uses discrete-time sensitivity analysis to explain how these errors arise. The authors propose time-scale separated online adaptation policies to mitigate distribution shift, validated on a nonlinear Duffing oscillator, showing that bounded continuous learning can prevent trajectory divergence while static deployments cannot.
By Vittorio Lippi
The paper presents a scalable learned iterative solver for nonlinear model predictive control (NMPC) that removes dependence on the prediction horizon by exploiting temporal decomposition in the network architecture. It introduces a closed‑loop resampling strategy to align training with deployment, achieving up to four‑fold speedups over IPOPT while maintaining comparable control performance and avoiding constraint violations. The method outperforms the previous monolithic LISCO approach on a CSTR cascade benchmark, scaling to larger problems without increasing network size.
By Lukas L\"uken, Sergio Lucia