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Xing-Gang Yan
Researcher at University of Kent
Publications - 204
Citations - 4723
Xing-Gang Yan is an academic researcher from University of Kent. The author has contributed to research in topics: Nonlinear system & Sliding mode control. The author has an hindex of 33, co-authored 195 publications receiving 3985 citations. Previous affiliations of Xing-Gang Yan include University of Hong Kong & University of Leicester.
Papers
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Journal ArticleDOI
Modeling and Dynamic Behavior of eIF2 Dependent Regulatory System With Disturbances
TL;DR: A mathematical model of eIF2-dependent regulatory system is used to identify the stability-conferring features within the system with the help of direct and indirect methods of Lyapunov stability theory and indicates that, the stability is a collective property and damage in the structure of the system changes the stability of thesystem.
Journal ArticleDOI
Decentralised Observation Using Higher Order Sliding Mode Techniques
TL;DR: In this article, a decentralised observer scheme is proposed for a class of nonlinear interconnected systems based on higher order sliding mode techniques, where the observer is continuous and chattering can be avoided.
Proceedings ArticleDOI
Adaptive output feedback finite time control for a class of second order nonlinear systems
TL;DR: A finite time output feedback based control scheme for a class of nonlinear second order systems and the design of a finite time observer based on the well-established step-by-step sliding mode observer design approach is developed.
Journal ArticleDOI
Adaptive Position Tracking Compensation for High-Speed Trains with Actuator Failures
TL;DR: In this article, an adaptive failure compensation for high-speed trains with traction system actuator failures to achieve the position tracking is proposed for high speed trains with a piecewise constant model.
Patent
Micro-fault diagnosis method for high-speed train traction system
TL;DR: In this paper, a micro-fault diagnosis method for a high-speed train traction system was proposed, which comprises the steps of extracting a first data subset X1, the first data subsets X1 is input into a first model set, training the first model sets for the first time, obtaining a second model set and finally, inputting fault data into the model set II to generate second feature data.