Zobrazeno 1 - 10
of 412
pro vyhledávání: '"Juntao Fei"'
Autor:
Jiapeng Xie, Juntao Fei
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract To maintain the vibrations of the gyroscope proof mass, an adaptive super-twisting sliding mode control (STSMC) of a micro gyroscope based on a two-loop recursive fuzzy neural network (TLRFNN) is designed. In order to estimate the unknown pa
Externí odkaz:
https://doaj.org/article/6e37fc455b494a87968ef215a6559b19
Publikováno v:
IEEE Access, Vol 12, Pp 164942-164952 (2024)
This paper provides a Self-adjustment Interval Type II Neural Network (SAIT2NN) method combined with Super-Twisting sliding mode control (STSMC), aiming at solving the harmonic current problem and improving power quality in an active power filter (AP
Externí odkaz:
https://doaj.org/article/b2193933df5b479c85f1741bd630d75c
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 930-938 (2023)
Aiming at the problem of high line loss caused by the access station of distributed generation (DG), a two-stage coordinated line loss reduction model based on elephant herding optimization (EHO) and second-order cone programming (SOCP) was put forwa
Externí odkaz:
https://doaj.org/article/36e11197e4f04402a59ceaf8bb9df358
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3271 (2024)
This research introduces an improved control strategy for an active power filter (APF) system. It utilizes an adaptive super-twisting sliding mode control (STSMC) scheme. The proposed approach integrates an interval type-2 fuzzy neural network with a
Externí odkaz:
https://doaj.org/article/537980eacd3f4285a8ef8c1a25cdd227
Publikováno v:
IEEE Access, Vol 10, Pp 42396-42403 (2022)
This study develops an adaptive Super-Twisting sliding mode control (STSMC) approach using an output feedback fuzzy neural network (OFFNN) for dynamic systems. The OFFNN approximator is designed to approach the model uncertainty, and a signal feedbac
Externí odkaz:
https://doaj.org/article/68b581b3201d4600a8ef4f88e5178597
Autor:
Xiaoyu Gong, Juntao Fei
Publikováno v:
Sensors, Vol 23, Iss 17, p 7450 (2023)
In this paper, an adaptive backstepping terminal sliding mode control (ABTSMC) method based on a double hidden layer recurrent neural network (DHLRNN) is proposed for a DC-DC buck converter. The DHLRNN is utilized to approximate and compensate for th
Externí odkaz:
https://doaj.org/article/80006b6109c84ea6b7705f24c71af862
Publikováno v:
Mathematics, Vol 11, Iss 14, p 3231 (2023)
A novel intelligent complementary sliding mode control (ICSMC) method is proposed for nonlinear systems with unknown uncertainties in this paper. A self-evolving Chebyshev radial basis function neural network (RBFNN) (SECRBFNN) with self-learning par
Externí odkaz:
https://doaj.org/article/05e12741f1874271b2cf128b7438340e
Publikováno v:
Mathematics, Vol 11, Iss 13, p 2835 (2023)
This paper designs a novel smooth super-twisting extended-state observer (SSTESO)-based smooth super-twisting sliding-mode control (SSTSMC) scheme to promote the robust ability and voltage-tracking performance of DC-DC buck converters. First, an SSTE
Externí odkaz:
https://doaj.org/article/1cb3460e7fa848fdbe1c35e594b9a31d
Publikováno v:
Mathematics, Vol 11, Iss 12, p 2785 (2023)
Aiming at the unknown uncertainty of an active power filter system in practical operation, combining the advantages of self-feedback structure, interval type-2 fuzzy neural network, and super-twisting sliding mode, an adaptive super-twisting sliding
Externí odkaz:
https://doaj.org/article/76b5d78e13c94fb6887a142f57214cbf
Publikováno v:
Mathematics, Vol 11, Iss 6, p 1495 (2023)
This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filt
Externí odkaz:
https://doaj.org/article/7f748d1b67ce4051ba9d8b82e78463a6