Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks

• URL: http://arxiv.org/abs/2507.02897v1
• Date: Sat, 21 Jun 2025 22:21:26 GMT
• Title: Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks
• Authors: Nathaniel Chen, Cheolsik Byun, Azarakash Jalalvand, Sangkyeun Kim, Andrew Rothstein, Filippo Scotti, Steve Allen, David Eldon, Keith Erickson, Egemen Kolemen,
• Abstract summary: This study implements and validates a real-time AI enabled linear and interpretable control system for successful divertor detachment control.<n>We demonstrate feedback divertor detachment control with a mean absolute difference of 2% from the target for both detachment and reattachment.
• Score: 0.981937495272719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI enabled linear and interpretable control system for successful divertor detachment control with the DIII-D lower divertor camera. Using D2 gas, we demonstrate feedback divertor detachment control with a mean absolute difference of 2% from the target for both detachment and reattachment. This automatic training and linear processing framework can be extended to any image based diagnostic for regulatory compliant controller necessary for future fusion reactors.

Related papers

• Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3) [0.0]
  This paper proposes a reinforcement learning framework for stabilizing the Twin Rotor Aerodynamic System (TRAS)<n>The complex dynamics and non-linear characteristics of the TRAS make it challenging to control using traditional control algorithms.<n>Experiments on a laboratory setup were carried out to confirm the controller's effectiveness in real-world applications.
  arXiv  Detail & Related papers  (2025-12-15T14:10:04Z)
• ScaleWeaver: Weaving Efficient Controllable T2I Generation with Multi-Scale Reference Attention [86.93601565563954]
  ScaleWeaver is a framework designed to achieve high-fidelity, controllable generation upon advanced visual autoregressive( VAR) models.<n>The proposed Reference Attention module discards the unnecessary attention from image$rightarrow$condition, reducing computational cost.<n>Experiments show that ScaleWeaver delivers high-quality generation and precise control while attaining superior efficiency over diffusion-based methods.
  arXiv  Detail & Related papers  (2025-10-16T17:00:59Z)
• Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems [0.0]
  We present an explainable AI-enhanced supervisory control framework for multi-agent robotics.<n>We validated the approach in two contrasting domains, spacecraft formation flying and autonomous underwater vehicles.
  arXiv  Detail & Related papers  (2025-09-18T23:59:13Z)
• LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion [20.022547219190013]
  Video Diffusion Models (VDMs) have demonstrated remarkable capabilities in synthesizing realistic videos by learning from large-scale data.<n>We propose LiON-LoRA, a novel framework that rethinks LoRA fusion through three core principles: Linear scalability, Orthogonality, and Norm consistency.<n> Experiments demonstrate that LiON-LoRA outperforms state-of-the-art methods in trajectory control accuracy and motion strength adjustment, achieving superior generalization with minimal training data.
  arXiv  Detail & Related papers  (2025-07-08T05:00:17Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)
• Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
  In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency. In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution. Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
  arXiv  Detail & Related papers  (2024-04-05T17:58:37Z)
• A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking [8.362739554991073]
  We present an exploration and assessment of employing a centralized deep Q-network (DQN) controller as a substitute for PID controllers. Our primary focus centers on illustrating this transition with the specific case of underwater object tracking. Our experiments, conducted within a Unity-based simulator, validate the effectiveness of a centralized RL agent over separated PID controllers.
  arXiv  Detail & Related papers  (2024-01-29T23:14:15Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers [49.689566246504356]
  We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. TransFusion achieves state-of-the-art performance on large-scale datasets. We extend the proposed method to the 3D tracking task and achieve the 1st place in the leaderboard of nuScenes tracking.
  arXiv  Detail & Related papers  (2022-03-22T07:15:13Z)
• Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control [0.9023847175654602]
  Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Initiative action state augmentation (IASA) for actor-critic-based RL controllers is introduced. This augmentation does not require any expert knowledge, leaving the approach model free.
  arXiv  Detail & Related papers  (2022-01-31T16:29:19Z)
• Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments [0.37798600249187286]
  We show that DRL can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics. We deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller.
  arXiv  Detail & Related papers  (2021-11-07T19:07:46Z)
• Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging [0.0]
  We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop predictive control. We show in simulations that our algorithm can also be applied to the control of a high-order deformable mirror.
  arXiv  Detail & Related papers  (2021-08-24T10:02:55Z)
• LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation [78.74202673902303]
  We propose a coarse-tofine LiDAR and camera fusion-based network (termed as LIF-Seg) for LiDAR segmentation. The proposed method fully utilizes the contextual information of images and introduces a simple but effective early-fusion strategy. The cooperation of these two components leads to the success of the effective camera-LiDAR fusion.
  arXiv  Detail & Related papers  (2021-08-17T08:53:11Z)
• Control Design of Autonomous Drone Using Deep Learning Based Image Understanding Techniques [1.0953917735844645]
  This paper presents a new framework to use images as the inputs for the controller to have autonomous flight, considering the noisy indoor environment and uncertainties. A new Proportional-Integral-Derivative-Accelerated (PIDA) control with a derivative filter is proposed to improve drone/quadcopter flight stability within a noisy environment.
  arXiv  Detail & Related papers  (2020-04-27T15:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.