Introduction to Grey system theory
The Journal of Grey System
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Analytical structure of a fuzzy controller with linear control rules
Information Sciences—Intelligent Systems: An International Journal
PID type fuzzy controller and parameters adaptive method
Fuzzy Sets and Systems
Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Brief paper: Communication and control co-design for networked control systems
Automatica (Journal of IFAC)
An initial study of gain-scheduling controller design for NCS using delay statistical model
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Automatica (Journal of IFAC)
A hybrid Grey & ANFIS approach to bullwhip effect in supply chain networks
WSEAS TRANSACTIONS on SYSTEMS
Robust Stability of Multi-variable Networked Control Systems with Random Time Delay
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Stabilization criterion based on new Lyapunov functional candidate for networked control systems
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Stability analysis and numerical simulation for a class of multi-variable networked control systems
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
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This paper presents a grey-fuzzy predictive controller that is based on fuzzy theory, grey prediction and on-line switching algorithms. The grey predictor is applied to extract key information and reduce the randomness of the measured non-stationary time-series signals from sensors, and send the prediction information to the fuzzy controller. The complete mathematical model is derived and the sufficient condition for convergence is given. To achieve better transient performance and steady-state responses, an on-line switching mechanism is adopted to regulate appropriately the forecasting step size of the grey predictor, according to the error feedback from different periods of the system response. Experimental results obtained from a plant show that the control accuracy and robustness are much improved when the proposed new method is applied.