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Zhao J.,Beijing Aritime Intelligent Control Co.
2013 25th Chinese Control and Decision Conference, CCDC 2013

Take Xiaoguanzhuang Iron Mine of LuZhong as the research object, introduce design ideas, system environment, hardware configuration of automatic control system for mine well hoist, account the technological process of the system and communication configuration of FM458 and ABB ACS6000C for the important. Automatic control system for mine well hoist is a high safety system, and for the important is to control exactly. This system make a curve for travel control, let the hoist device move according to the curve, and has multiple protection to ensure hoist device stop at unload position exactly, only has 0.01m error. This passage use block diagram make reader easily understand the constitute and communication of the whole system. © 2013 IEEE. Source

Song X.,Beijing Aritime Intelligent Control Co. | Hu Z.,University Of Science And Technology Liaoning | He X.,University Of Science And Technology Liaoning | Tu L.,University Of Science And Technology Liaoning
Advanced Materials Research

The prediction and control of the hot strip's width is one of the key factors to reducing metal loss in hot roughing. Bayesian approach can control the parameters of neural network by calculating some super-parameters. This paper proposes a prediction model of the hot-rolled steel strip's width based on Bayesian Neural Network, through application on the data of a 1500mm steel rolling production line in China, the MAE (Mean Absolute Error) between the predicted width value and real width value less than 10mm, this result shows that the precision of prediction is superior to some traditional mathematical model such as BP-neural networks. In this paper, we conclude that Bayesian neural network can improve the forecast precision of the hot strip's width. © (2013) Trans Tech Publications, Switzerland. Source

Sun X.-X.,Jilin Agricultural Science and Technology College | An C.,Beijing Aritime Intelligent Control Co. | Wang S.,Jilin Normal University
2010 Chinese Control and Decision Conference, CCDC 2010

This paper considers the problem of robust generalized H2 filtering for discrete-time linear systems with polytopic uncertainties. Based on parameter-dependent Lyapunov functions combined with Finsler's lemma, new conditions for the solvability of the problem are given in terms of linear matrix inequalities (LMIs). Compared to the existing results, more slack variables are introduced to provide extra freedom for the generalized H 2 optimization which lead to improving the performance and reducing the conservatism further. An example is give to illustrate the effectiveness of the proposed method. ©2010 IEEE. Source

Wang S.,Jilin Normal University | An C.,Beijing Aritime Intelligent Control Co. | Sun X.-X.,Jilin Agricultural Science and Technology College | Du X.,Shanghai University
2010 Chinese Control and Decision Conference, CCDC 2010

This paper is concerned with the average consensus problem of networked multi-agent systems over memory communication channels with packet losses. The focus is on a particular case: uniform packet losses(i.e. all the communication links in the network may be failed simultaneously). The objective is to compare the performance of the corresponding memoryless and memory consensus protocols. We show that the memory consensus protocol always provides a better performance than the corresponding memoryless one, moreover, greater possibility of data drop will produces greater convergence rate gap between the memory and memoryless protocols. Simulations are included for illustration. ©2010 IEEE. Source

Han M.,Dalian University of Technology | Zhao Y.,Dalian University of Technology | Yang X.-L.,Beijing Aritime Intelligent Control Co. | Lin D.,Steelmaking Plant of Benxi Steel Sheet Co.
Kongzhi Lilun Yu Yingyong/Control Theory and Applications

To deal with the problem that the classical relevance vector machine is sensitive to outliers, we present a novel robust relevance vector machine. This machine is applied to predict the endpoint carbon content and temperature of the basic-oxygen-furnace(BOF) steelmaking. Each training sample is assumed to have its individual coefficient of noise variance. With the increase of the prediction error during training procedure, the coefficients of outliers gradually decrease, reducing the impact of outliers. In addition, the iterative formulas for the optimization of hyper-parameters are derived in the Bayesian evidence framework. Simulation results of benchmark test data and the BOF steelmaking data show that the proposed mode achieves high prediction accuracy and good robustness. Source

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