Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Yingxin Shou"'
Publikováno v:
International Journal of Robust and Nonlinear Control. 33:4262-4280
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1687d9dac998903c049ad00924bedd8
https://doi.org/10.1002/rnc.6607
https://doi.org/10.1002/rnc.6607
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:6030-6037
This article concentrates on the event-based collaborative design for strict-feedback systems with uncertain nonlinearities. The controller is designed based on neural network (NN) weights adaptive law. The controller and NN weights adaptive law are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ae13de08e56a00f28c211f24129db4e
https://doi.org/10.1109/tnnls.2021.3072107
https://doi.org/10.1109/tnnls.2021.3072107
Publikováno v:
Neurocomputing. 484:142-148
A sliding mode based fault-tolerant control method using neural learning is studied for hypersonic reentry vehicle (HRV) in this paper. Based on the non-singular second-order terminal sliding mode, the composite neural learning is adopted to deal wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4dfcb875d7ac38cefe18d8ff08cda93b
https://doi.org/10.1016/j.neucom.2021.10.084
https://doi.org/10.1016/j.neucom.2021.10.084
Publikováno v:
International Journal of Robust and Nonlinear Control. 32:2883-2901
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e21284bca3480e301a0cd48accf854ca
https://doi.org/10.1002/rnc.5641
https://doi.org/10.1002/rnc.5641
Publikováno v:
IEEE transactions on cybernetics.
This article investigates the finite-time control of the strict-feedback nonlinear system using composite learning based on the historical stack. The controller design adopts the backstepping scheme while the nonlinear function is introduced to avoid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee10c3b9974aad70cddf0568f40ea665
https://pubmed.ncbi.nlm.nih.gov/35895658
https://pubmed.ncbi.nlm.nih.gov/35895658
Publikováno v:
IEEE transactions on neural networks and learning systems.
This paper investigates the predefined-time hierarchical coordinated adaptive control on the hypersonic reentry vehicle in presence of low actuator efficiency. In order to compensate for the deficiency of rudder deflection in advantage of channel cou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94e7664ee635006cab01c70d0d874ebb
https://pubmed.ncbi.nlm.nih.gov/35298383
https://pubmed.ncbi.nlm.nih.gov/35298383
Publikováno v:
Mathematical Methods in the Applied Sciences.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b631b1081620596309270ea7239410d3
https://doi.org/10.1002/mma.8071
https://doi.org/10.1002/mma.8071
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 30:1296-1307
This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc1a58042a1b70f9d1789324c253a420
https://doi.org/10.1109/tnnls.2018.2862907
https://doi.org/10.1109/tnnls.2018.2862907
Publikováno v:
IEEE transactions on neural networks and learning systems. 33(11)
The tracking control is investigated for a class of uncertain strict-feedback systems with robust design and learning systems. Using the switching mechanism, the states will be driven back by the robust design when they run out of the region of adapt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a18de0c7fcda182e9ef7245866020a62
https://pubmed.ncbi.nlm.nih.gov/33945488
https://pubmed.ncbi.nlm.nih.gov/33945488
Autor:
Bin Xu, Yingxin Shou
Publikováno v:
IEEE Transactions on Industrial Electronics. 65:6414-6424
Considering the unknown dynamics of the multiple-input-multiple-output strict-feedback nonlinear systems, this paper proposed the neural composite learning control using the online recorded data. The control structure follows the back-stepping scheme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac422f352c8ba1add40da1dfb5e6932f
https://doi.org/10.1109/tie.2018.2793207
https://doi.org/10.1109/tie.2018.2793207