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Fu Y.,East China Jiaotong University | Fu Y.,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 | And 2 more authors.
Information Sciences | Year: 2017

High-speed electric multiple unit (HSEMU) is an advanced transportation system, which share some special characteristics such as nonlinear dynamics, complex working environment and time-varying operational conditions. To achieve a safe, punctual and automatic running of HSEMU, effective modeling methods of HSEMU and real-time optimal control schemes are useful to further improve the running performances. This paper aims to develop intelligent control techniques for speed tracking control of HSEMU. The well-known adaptive neuro-fuzzy inference system (ANFIS) is employed to model the dynamics, and an ANFIS-based generalized predictive controller is proposed with a stability analysis of the closed-loop system. Experimental results with comparisons on real-world running data demonstrate that our proposed techniques in this work can perform favorably in terms of both safety and punctuality. © 2016 Elsevier Inc.


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.


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.


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.


Yang H.,East China Jiaotong University | Yang H.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | He L.,East China Jiaotong University | He L.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | And 6 more authors.
Information Sciences | Year: 2016

A new multiple-model predictive control scheme is proposed for the control of the component content in the rare earth extraction process. Multiple local linear models with two-input and two-output of the rare earth countercurrent extraction process are constructed, each of which is established under an operation condition. A component content predictive controller is designed for a local linear model, based on the dynamic compensation of the extraction liquid flow and the scrubbing liquid flow. A switching algorithm based on the minimum accumulative error is formed to select the most appropriate model and controller to meet the productive purity requirements. Simulation results for the CePr/Nd countercurrent extraction process are presented to show the desired performance of the proposed approach. © 2016 Elsevier Inc.


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.


Yang H.,East China Jiaotong University | Yang H.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Liu H.-E.,East China Jiaotong University | Liu H.-E.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | And 2 more authors.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA) | Year: 2015

The electric multiple units (EMU) provide a transport service in the dynamical running environment, which should meet the requirements of safety, punctuality, precise train stopping, energy conservation and comfort simultaneously. However, since pervious manual operation method of EMU is mainly based on a given V-S curve (velocity versus position curve) and drivers' experience, it cannot meet the multi-objective operation requirements in real time. In order to improve the operation strategy, this paper develops a multi-objective online optimization model for the EMU operation based on speed limit curve. Then, we optimize the operation strategy using a modified multi-objective particle swarm optimization algorithm on line, so as to obtain the Pareto optimal solution set. Further, based on the delay state of EMU running process, we pick out the optimal operation strategy from the Pareto set. Finally, the running process of the EMU operated on the optimal operation strategy can satisfy the multi-objective requirements. And the experimental results on the field data of CRH380AL (China railway high-speed EMU type-380AL) running process show the real time effectiveness of the proposed approach. © 2014 IEEE.


Yang G.,East China Jiaotong University | Yang G.,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 | And 2 more authors.
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2015

For solving some process control engineering problems which can be treated as a time-series, a fast and accurate self-organization learning strategy is proposed based on the significance evaluation of hidden neurons with respect to the network output. This approach is introduced to optimize the architecture and parameters of span-lateral inhibition neural network (S-LINN) simultaneously. The insignificant neuron(s) will be pruned automated step by step based on the determination of significance index. The proposed self-organizing approach has been tested on one time-series prediction benchmark problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.


Yang H.,East China Jiaotong University | Yang H.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | Fu Y.-T.,East China Jiaotong University | Fu Y.-T.,Key Laboratory of Advanced Control and Optimization of Jiangxi Province | And 4 more authors.
Control Engineering Practice | Year: 2014

The high-speed electric multiple unit (EMU) is a complex, uncertain and nonlinear dynamic system. The traditional approach to operating the high-speed EMU is based upon manual operation. To improve the performance of high-speed EMU, this paper develops a control dynamic model to capture the motion of the high-speed EMU and then uses it to design a desirable speed tracking controller for EMU. We exploit a data-driven adaptive neurofuzzy inference system (ANFIS) to model the running process. Based on the ANFIS model, we propose a generalized predictive control algorithm to ensure the high-precision speed tracking of the high-speed EMU. The simulation results on the actual CRH380AL (China railway high-speed EMU type-380AL) operation data show that the proposed approach could ensure the safe, punctual, comfortable and efficient operation of high-speed EMU. © 2013.


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.

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