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Chen S.-M.,East China Jiaotong University | Chen S.-M.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Pang S.-P.,East China Jiaotong University | Zou X.-Q.,East China Jiaotong University
Chinese Physics B | Year: 2013

Based on the relationship between capacity and load, cascading failure on weighted complex networks is investigated, and a load-capacity optimal relationship (LCOR) model is proposed in this paper. Compared with three other kinds of load-capacity linear or non-linear relationship models in model networks as well as a number of real-world weighted networks including the railway network, the airports network and the metro network, the LCOR model is shown to have the best robustness against cascading failure with less cost. Furthermore, theoretical analysis and computational method of its cost threshold are provided to validate the effectiveness of the LCOR model. The results show that the LCOR model is effective for designing real-world networks with high robustness and less cost against cascading failure. © 2013 Chinese Physical Society and IOP Publishing Ltd. Source


Tan C.,East China Jiaotong University | Tan C.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Yang H.,East China Jiaotong University | Yang H.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Tao G.,University of Virginia
Information Sciences | Year: 2016

This paper develops a multivariable multiple-model adaptive control scheme for adaptive state feedback state tracking control of systems whose plant-model matching conditions are uncertain and parameters are unknown. To deal with the uncertainty of plant-model matching conditions needed for adaptive control, multiple reference model systems are employed to generate multiple parameter estimation and feedback control signals from which a most suitable control input is selected by a control switching mechanism designed using multiple estimation errors. Such a new multiple-model control design is based on an expanded control system parametrization which has the capacity to cover system structural uncertainties. Stability analysis and simulation results ensure and verify the desired adaptive control system stability and tracking performance. © 2016 Elsevier Inc. Source


Lu R.,Nanchang University | Lu R.,East China Jiaotong University | Lu R.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Yang H.,Nanchang University | And 2 more authors.
Chinese Journal of Chemical Engineering | Year: 2015

For measurement of component content in the extraction and separation process of praseodymium/neodymium (Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted, which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine (LSSVM) model for Nd (Pr) content, while the model parameters are determined with the GA algorithm. To improve the adaptability of the model, the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction. © 2015 Elsevier B.V. Source


Yang H.,East China Jiaotong University | Yang H.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Zhang K.-P.,East China Jiaotong University | Zhang K.-P.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | And 3 more authors.
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | Year: 2012

In the continuous high-speed operation, severe demands on reliability and disturbance-rejection are needed by the high speed trains. According to its nonlinear dynamic characteristics and operation data, we build a set of multiple models for the high-speed train by using subtractive clustering and pattern classification algorithm. To adapt to the change of object and disturbance characteristics, we use a model switching scheme to select online, from this set of models, the optimal model with smallest model accumulative error. On the basis of this optimal model, we design the active fault tolerant predictive controller to realize the secure and efficient operations of the high-speed train. Simulation example is given to show the effectiveness of this method. Source


Yang H.,East China Jiaotong University | Yang H.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Liu H.,East China Jiaotong University | Liu H.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | And 2 more authors.
Neurocomputing | Year: 2015

The electric multiple unit (EMU) is a complex system running in dynamic environments. Satisfaction on real-time manual operation strategy of the EMU with respect to the multi-objective operation demands, including security, punctuality, accurate train parking, energy saving and ride comfort, depends on the drivers' experience and a given V-S curve (velocity versus position curve). To improve the operation strategy, a multi-objective optimization model of EMU operation is developed on the basis of dynamic analysis and speed restriction mutation. Using a modified particle swarm optimization algorithm, a Pareto optimal solution set is obtained by the online optimization of the EMU's operation strategy. Finally, according to the preference order ranking, an optimal operation strategy is sorted out from the Pareto set which satisfies the multi-objective requirements in real time. Experimental results on the field data of CRH380AL (China's railway high-speed EMU type-380AL) demonstrate the effectiveness of the proposed approach. © 2015 Elsevier B.V. Source

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