Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Meizhen Xia"'
Publikováno v:
Journal of the Franklin Institute. 359:9734-9758
Publikováno v:
International Journal of Robust and Nonlinear Control. 32:9307-9331
Publikováno v:
International Journal of Robust and Nonlinear Control. 30:4979-5003
Autor:
Meizhen Xia, Tianping Zhang
Publikováno v:
International Journal of Adaptive Control and Signal Processing. 33:1079-1096
Summary Stochastic adaptive dynamic surface control is presented for a class of uncertain multiple‐input–multiple‐output (MIMO) nonlinear systems with unmodeled dynamics and full state constraints ...
Autor:
Tianping Zhang, Meizhen Xia
Publikováno v:
Journal of the Franklin Institute. 356:129-146
This paper solves the problem of adaptive neural dynamic surface control (DSC) for a class of full state constrained stochastic nonlinear systems with unmodeled dynamics. The concept of the state constraints in probability is first proposed and appli
Autor:
Meizhen Xiao, Amar Razzaq, Ping Qing, Wasin Phromphithakkul, Rajermani Thinakaran, Mohamad Alnafissa
Publikováno v:
Frontiers in Sustainable Food Systems, Vol 8 (2024)
Food waste due to consumer rejection of aesthetically imperfect produce poses significant challenges to food security and environmental sustainability. We construct a matching model between the marketing message framing of ugly produce and the contro
Externí odkaz:
https://doaj.org/article/15a79800f2ad4887ada2f42b445d7975
Publikováno v:
2020 39th Chinese Control Conference (CCC).
In this paper, the problem of adaptive neural control is discussed for multi-input multi-output (MIMO) strict feedback nonlinear systems with time-varying output constraints and unmodeled dynamics. Using the composite function of hyperbolic tangent f
Publikováno v:
International Journal of Adaptive Control and Signal Processing. 32:1731-1747
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems. 47:2378-2387
In this paper, adaptive neural dynamic surface control (DSC) is developed using radial basis function neural networks (NNs) for a class of pure-feedback nonlinear systems with full state constraints and dynamic uncertainties. Based on a one-to-one no
Publikováno v:
Automatica. 81:232-239
In this paper, the problem of adaptive neural network (NN) dynamic surface control (DSC) is discussed for a class of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics. By introducing a one to one nonlinear mapping,