Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems-Reference-Cited by-同舟云学术

Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems

Published:2022 Issue: Volume: Page:1-13
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Tran Vu Phi¹^ORCID,Mabrok Mohamed A.²^ORCID,Anavatti Sreenatha G.¹^ORCID,Garratt Matthew A.¹^ORCID,Petersen Ian R.³

Affiliation:

1. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2610, Australia.

2. School of Engineering, Australian College of Kuwait, Safat 13015, Kuwait.

3. Research School of Electrical Energy and Materials Engineering, ANU College of Engineering and Computer Science, Canberra, ACT 2601, Australia.

Funder

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/6352949/09789160.pdf?arnumber=9789160

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