Paper Citation Record · LEDGER

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2506.08983.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.08983 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:03:04.380102Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact8
verified fuzzy3
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 191ee5c3-46cb-41f6-8428-a159cf5b9b43 · outbound

This paper cites Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:03:05.918620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14ab0bfd-49b9-4053-ae8c-7daee48ae58e · outbound

This paper cites Adaptive Data-Driven Control for Linear Time Varying Systems,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Adaptive Data-Driven Control for Linear Time Varying Systems,

Reference 2

Resolution: verified exact; doi, observed 2026-08-07T05:03:05.749600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 01e2cdbb-f26e-4d27-9a45-cfb1711a19ac · outbound

This paper cites Uncertainty-Aware Data-driven Tobacco Loosening and Conditioning Process Moisture Prediction and Control Optimization,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Uncertainty-Aware Data-driven Tobacco Loosening and Conditioning Process Moisture Prediction and Control Optimization,

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-07T05:03:07.361476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd19282a-84aa-4254-843c-619072616305 · outbound

This paper cites The added water control system based on neural network model and double parameter corrected for loosening and conditioning cylinder,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning The added water control system based on neural network model and double parameter corrected for loosening and conditioning cylinder,

Reference 4

Resolution: verified exact; doi, observed 2026-08-07T05:03:05.574927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84db4a55-713d-4a8c-85d2-e6804a78a541 · outbound

This paper cites An Operation Mode Optimization Method for Tobacco Loosening and Conditioning Process Based on Batch Clustering and KNN Algorithm,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning An Operation Mode Optimization Method for Tobacco Loosening and Conditioning Process Based on Batch Clustering and KNN Algorithm,

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:03:07.128130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a783ae0e-b6ba-4ad5-af84-0d85f61764b4 · outbound

This paper cites Construction and application of the prediction model of outlet moisture in the loosening and conditioning process,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Construction and application of the prediction model of outlet moisture in the loosening and conditioning process,

Reference 6

Resolution: verified exact; doi, observed 2026-08-07T05:03:05.377076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f31b6a52-2e39-461e-9f92-60f6244c5b38 · outbound

This paper cites Design of an optimal scheduling control system for smart manufacturing processes in tobacco industry,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Design of an optimal scheduling control system for smart manufacturing processes in tobacco industry,

Reference 7

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:03:06.921069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0e81d097-6271-40c7-87fb-01b216dae4cf · outbound

This paper cites A tobacco moisture prediction approach based on VAE and PSO-BiLSTM,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning A tobacco moisture prediction approach based on VAE and PSO-BiLSTM,

Reference 8

Resolution: verified exact; doi, observed 2026-08-07T05:03:05.209813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7b0dcb79-741d-46b3-aafe-3e207b72c5c4 · outbound

This paper cites A Novel Variable Exponential Discrete Time Sliding Mode Reaching Law,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning A Novel Variable Exponential Discrete Time Sliding Mode Reaching Law,

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:03:06.706133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5af0e453-964b-4383-92f5-c2b52428ff6f · outbound

This paper cites Control-Coherent Koopman Modeling: A Physical Modeling Approach.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Control-Coherent Koopman Modeling: A Physical Modeling Approach

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:03:04.976328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9a2ddde0-bf14-40ef-bba2-26866b28771e · outbound

This paper cites Machine learning approach to observability analysis of high-dimensional nonlinear dynamical systems using Koopman operator theory,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Machine learning approach to observability analysis of high-dimensional nonlinear dynamical systems using Koopman operator theory,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:08.050807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b2e72b41-beba-4dfd-a4d8-3cb2e0bba4cb · outbound

This paper cites Extended dynamic mode decomposition with learned Koopman eigenfunctions for prediction and control,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Extended dynamic mode decomposition with learned Koopman eigenfunctions for prediction and control,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:03.165608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3d13af2-2aff-4bec-a6c7-b91957ae5373 · outbound

This paper cites Learning model predictive control of nonlinear systems with time-varying parameters using Koopman operator,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Learning model predictive control of nonlinear systems with time-varying parameters using Koopman operator,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:07.879198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74ecd413-42b8-4d4e-ba09-c16e06f091bf · outbound

This paper cites Model Predictive Traction Control System Based on the Koopman Operator,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Model Predictive Traction Control System Based on the Koopman Operator,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:03.401163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1be4a03-459c-4992-898d-dbfa412bee5d · outbound

This paper cites Robust Koopman-MPC Approach with High-Order Disturbance Observer for Control of Pneumatic Soft Bending Actuators under External Loads,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Robust Koopman-MPC Approach with High-Order Disturbance Observer for Control of Pneumatic Soft Bending Actuators under External Loads,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:03.532169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95917909-af68-4613-b44c-4225a2bab419 · outbound

This paper cites A MPC Performance Degradation Diagnosis Method Based on Receding Feature Horizon,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning A MPC Performance Degradation Diagnosis Method Based on Receding Feature Horizon,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:07.706912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43a45938-efed-4148-acb9-b2c157059b0a · outbound

This paper cites A Deep Reinforcement Learning Approach to Improve the Learning Performance in Process Control,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning A Deep Reinforcement Learning Approach to Improve the Learning Performance in Process Control,

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T05:03:04.662722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5ceb226-f443-4b9b-a68e-f6d1d1037df0 · outbound

This paper cites Safe Reinforcement Learning With Dual Robustness,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Safe Reinforcement Learning With Dual Robustness,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:03.759345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6f8ac98-85ba-4bfc-982c-2837f6a708bc · outbound

This paper cites A Review of Safe Reinforcement Learning: Methods, Theory and Applications.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning A Review of Safe Reinforcement Learning: Methods, Theory and Applications

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:03.922702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ef87b1f-9cc1-4e1c-8a8f-73eeade92ef6 · outbound

This paper cites an unresolved cited work.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:03:07.538570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1b4fdd42-8a6b-4b20-80dc-c34116c43ed2 · outbound

This paper cites On the design of persistently exciting inputs for data-driven control of linear and nonlinear systems,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning On the design of persistently exciting inputs for data-driven control of linear and nonlinear systems,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:04.251792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18541ef3-bfcb-40d8-ba00-846898a4f9a7 · outbound

This paper cites Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems,.

Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems,

Reference 22

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:03:06.156562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.