Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Yunmei Fang"'
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:
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:
IEEE Access, Vol 9, Pp 25681-25690 (2021)
This study proposed an adaptive Radical Basis Function (RBF) neural control strategy with a complementary sliding mode approach to compensate the harmonic current in an Active Power Filter (APF). A backstepping algorithm is incorporated to simplify t
Externí odkaz:
https://doaj.org/article/bd29a0209a064763b25dbfb9f961200d
Publikováno v:
Advances in Mechanical Engineering, Vol 14 (2022)
In this paper, a neural sliding mode control approach is developed to adjust the sliding gain using a radial basis function (RBF) neural network (NN) for the tracking control of Microelectromechanical Systems (MEMS) triaxial vibratory gyroscope. Firs
Externí odkaz:
https://doaj.org/article/16b2761594e84979896ba100263f6524
Publikováno v:
IEEE Access, Vol 8, Pp 96027-96035 (2020)
An adaptive backstepping fuzzy neural network (FNN) controller using a fuzzy sliding mode controller is designed to suppress the harmonics and improve the performance of a shunt active power filter (APF). A backstepping method transforms the APF syst
Externí odkaz:
https://doaj.org/article/6a68b314c8de48859057c21cdf33f1ab
Publikováno v:
Fractal and Fractional, Vol 6, Iss 9, p 482 (2022)
A second-order sliding mode control (SOSMC) with a fractional module using adaptive fuzzy controller is developed for an active power filter (APF). A second-order sliding surface using a fractional module which can decrease the discontinuities and ch
Externí odkaz:
https://doaj.org/article/98581fa233d143e4ab9d641127faaf4d
Publikováno v:
Symmetry, Vol 14, Iss 8, p 1704 (2022)
In practical applications, for highly nonlinear systems, how to implement control tasks for dynamic systems with uncertain parameters is still a hot research issue. Aiming at the internal parameter fluctuations and external unknown disturbances in no
Externí odkaz:
https://doaj.org/article/5d61866f61b94d808db092e922650291
Publikováno v:
Advances in Mechanical Engineering, Vol 12 (2020)
An adaptive H-infinity tracking control is proposed for a z-axis microgyroscope with system nonlinearities. All the signals can be guaranteed in a bounded range, and tracking error is uniformly ultimately bounded, an H-infinity tracking performance i
Externí odkaz:
https://doaj.org/article/2165c7eb95ca4b3f9d92cc0e9a57d3a2
Publikováno v:
Advances in Mechanical Engineering, Vol 11 (2019)
This article proposes an adaptive control scheme with a neural network compensator for controlling a micro-electro-mechanical system gyroscope with disturbance and model errors. The adaptive neural network compensator is used to compensate the nonlin
Externí odkaz:
https://doaj.org/article/4619232024bb44a0916bd51a1b6ceda0