Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints-Reference-Cited by-同舟云学术

Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints

Published:2024-07 Issue:7 Volume:20 Page:9465-9475
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Li Jinna¹^ORCID,Yang Mingwei¹^ORCID,Lewis Frank L.²^ORCID,Zheng Meng³^ORCID

Affiliation:

1. School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, P. R. China

2. UTA Research Institute, The University of Texas at Arlington, Arlington, TX, USA

3. Key Laboratory of Robotics and the Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

Funder

National Natural Science Foundation of China

Basic Research Project of Education Department of Liaoning Province

Liaoning Revitalization Talents Program

Natural Science Foundation of Liaoning Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10582275/10504644.pdf?arnumber=10504644

Reference27 articles.

1. Evolutionary Optimization Under Uncertainty: The Strategies to Handle Varied Constraints for Fluid Catalytic Cracking Operation

2. Compensation Signal Based Dual-Rate Operational Feedback Decoupling Control for Flotation Processes

3. Operational Optimal Tracking Control for Industrial Multirate Systems Subject to Unknown Disturbances

4. Multi-rate Layered Operational Optimal Control for Large-Scale Industrial Processes

5. Reinforcement Learning Based Optimal Control of Linear Singularly Perturbed Systems

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