A Generalized Dynamic Nonsmooth Control Design for DC Microgrids based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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A Generalized Dynamic Nonsmooth Control Design for DC Microgrids based on Deep Reinforcement Learning

Published:2023-08-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)
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Author:

Zhou Liwen¹,Zhang Chuanlin¹,Ge Jing¹,Dong Xin¹,Cui Chenggang¹

Affiliation:

1. Shanghai University of Electric Power,College of Automation Engineering,Shanghai,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10241359/10241362/10241895.pdf?arnumber=10241895

Reference18 articles.

1. Weights-varying MPC for Autonomous Vehicle Guidance: a Deep Reinforcement Learning Approach

2. Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

3. Deep Reinforcement Learning for Continuous Electric Vehicles Charging Control With Dynamic User Behaviors

4. DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation

5. A Nonsmooth Dynamic Control Approach for Series Elastic Actuator

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