Robust Control of Linear Systems: A Min-Max Reinforcement Learning Formulation-Reference-Cited by-同舟云学术

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Robust Control of Linear Systems: A Min-Max Reinforcement Learning Formulation

Published:2023-10-25 Issue: Volume: Page:
ISSN:
Container-title:2023 20th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
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Author:

Flores-Campos Juan Alejandro¹,Perrusquía Adolfo²

Affiliation:

1. Instituto Politécnico Nacional,UPIITA-IPN,Mexico City,Mexico

2. Cranfield University,School of Aerospace, Transport and Manufacturing,Bedford,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10332458/10332802/10332826.pdf?arnumber=10332826

Reference35 articles.

1. Optimal and Autonomous Control Using Reinforcement Learning: A Survey

2. Output Feedback Q-Learning Control for the Discrete-Time Linear Quadratic Regulator Problem

3. Solution of the linear quadratic regulator problem of black box linear systems using reinforcement learning

4. Learning Robust LQ-Controllers Using Application Oriented Exploration

5. Continuous-time reinforcement learning for robust control under worst-case uncertainty

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Novel Physics-Informed Recurrent Neural Network Approach for State Estimation of Autonomous Platforms;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Disaster Area Coverage Optimisation Using Reinforcement Learning;2024 International Conference on Unmanned Aircraft Systems (ICUAS);2024-06-04

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