The Power Optimization Control of Shearer Based on ANFIS

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Abstract:

Aiming at the power optimization control of shearer, a novel method by ANFIS was proposed to control the power of shearer. Firstly, the method based ANFIS was used to evaluate the current power state of the shearer, and then ANN MPC model was used to control the speed of shearer. By adjusting the speed of shearer in real time, it was to achieve the purpose of power optimization. Simulation results in Mat lab shown that the effectiveness and real-time of the method which was proposed in this thesis was verified.

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1727-1731

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January 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] ZhaoYue-jin, Qin-He. Preparing of Energy Efficiency Standards for Domestic Motors[J]. Sall & Medium Electric Machines. 2002. 29(2).

Google Scholar

[2] Cui Na-xin, Zhang Cheng-hui. Advances in Efficiency Optimization Control of Inverter-Fed Induction Motor Drives[J]. Transactions of China Electro Technical Society. 2004. 19(5).

Google Scholar

[3] LI Xiao-Huo. Constant Power Control of Shearer Cutting Motor Based on ES[J]. Application of computer system. Vol. 21(2012), pp.141-143.

Google Scholar

[4] ZhaoYi-hui. Constant Power Automatic Control System of Electric Haulage Shearer Based on Fuzzy Control [J]. Coal mine electromechanical. Vol. 12. (2012), pp.41-43.

Google Scholar

[5] Rajesh Singh, Ashutosh Kainthola, T.N. Singh. Estimation of elastic constant of rocks using an ANFIS approach[J]. Applied Soft Computing. 2012. 12(40-45).

DOI: 10.1016/j.asoc.2011.09.010

Google Scholar

[6] U. Sabura Banu, G. Uma. ANFIS based sensor fault detection for continuous stirred tank reactor [J]. Applied Soft Computing. 2011. 11 (2618-2624).

DOI: 10.1016/j.asoc.2010.10.009

Google Scholar

[7] Paisan Kittisupakorn, est. Neural network based model predictive control for a steel pickling process [J]. Journal of Process Control. Vol. 19. (2012), p.579–590.

DOI: 10.1016/j.jprocont.2008.09.003

Google Scholar

[8] VincentA. Akpan, GeorgeD. Hassapis. Nonlinear model identification and adaptive model predictive control using neural networks [J]. ISA Transactions . 2011(50): 177–194.

DOI: 10.1016/j.isatra.2010.12.007

Google Scholar