Game-Based Backstepping Design for Strict-Feedback Nonlinear Multi-Agent Systems Based on Reinforcement Learning-Reference-Cited by-同舟云学术

Game-Based Backstepping Design for Strict-Feedback Nonlinear Multi-Agent Systems Based on Reinforcement Learning

Published:2022 Issue: Volume: Page:1-14
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Long Jia¹^ORCID,Yu Dengxiu¹^ORCID,Wen Guoxing²^ORCID,Li Li³,Wang Zhen⁴^ORCID,Chen C. L. Philip⁵^ORCID

Affiliation:

1. Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.

2. College of Science, Binzhou University, Binzhou 256600, China, and also with the School of Mathematics and Statistics, Qilu University of Technology, Jinan 250353, China.

3. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China.

4. School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi'an 710072, China.

5. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China, and also with the Navigation College, Dalian Maritime University, Dalian 116026, China.

Funder

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

Key Technology Research and Development Program of Science and Technology-Scientific and Technological Innovation Team of Shaanxi Province

Fok Ying-Tong Education Foundation China

China Postdoctoral Science Foundation

Tencent Foundation and XPLORER PRIZE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09787792.pdf?arnumber=9787792

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