Xiuxia Yin

Dr. Xiuxia Yin

orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
4 October 2015 in International Journal of Control
Verified
Journal Article
Closed access
XYXiuxia Yin
Xiuxia Yin
Dong YueDong Yue
Dong Yue
SHSonglin Hu
Songlin Hu
This paper investigates the distributed adaptive event-triggered consensus control for a class of nonlinear agents. Each agent is subject to input saturation. Two kinds of distributed event-triggered control scheme are introduced, one is continuous-time-based event-triggered scheme and the other is sampled-data-based event-triggered scheme. Compared with the traditional event-triggered schemes in the existing literatures, the parameters of the event-triggered schemes in this paper are adaptively adjusted by using some event-error-dependent adaptive laws. The problem of simultaneously deriving the controller gain matrix and the event-triggering parameter matrix, and tackling the saturation nonlinearity is cast into standard linear matrix inequalities problem. A convincing simulation example is given to demonstrate the theoretical results.

1 April 2013 in Journal of the Franklin Institute
Verified
Journal Article
Closed access
XYXiuxia Yin
Xiuxia Yin
DYDong Yue
Dong Yue
SHSonglin Hu
Songlin Hu

1 June 2013 in Journal of the Franklin Institute
Verified
Journal Article
Closed access
XYXiuxia Yin
Xiuxia Yin
DYDong Yue
Dong Yue

1 September 2020 in Processes
Verified
Journal Article
Open access
PDF file
YFYichuan Fu
Yichuan Fu
Zhiwei GaoZhiwei Gao
Zhiwei Gao
LYLiu Yuanhong
Liu Yuanhong
AZAihua Zhang
Aihua Zhang
XYXiuxia Yin
Xiuxia Yin
In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark.

1 January 2016 in SIAM Journal on Control and Optimization
Verified
Journal Article
Closed access
XYXiuxia Yin
Xiuxia Yin
DYDong Yue
Dong Yue
SHSonglin Hu
Songlin Hu
CPChen Peng
Chen Peng
YXYusheng Xue
Yusheng Xue
This paper proposes a novel structure of networked control systems (NCSs) with communication logic, which incorporates model-based networked control systems (MB-NCSs), predictive control, and an ev...

3 December 2014 in International Journal of Robust and Nonlinear Control
Verified
Journal Article
Closed access
PDF file
XYXiuxia Yin
Xiuxia Yin
Dong YueDong Yue
Dong Yue
SHSonglin Hu
Songlin Hu