Control of the Electric Load Simulator Using Fuzzy Multiresolution Wavelet Neural Network with Dynamic Compensation
A fuzzy multiresolution wavelet neural network (FMWNN) controller with dynamic compensation (DC) is proposed to address the complexities of the electric load simulator (ELS). The FMWNN acts as a main torque tracking controller, which takes full advantage of the merits of an ideal sliding mode, fuzzy...
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Published in | Shock and vibration Vol. 2016; no. 2016; pp. 1 - 10 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2016
John Wiley & Sons, Inc Hindawi Limited |
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Abstract | A fuzzy multiresolution wavelet neural network (FMWNN) controller with dynamic compensation (DC) is proposed to address the complexities of the electric load simulator (ELS). The FMWNN acts as a main torque tracking controller, which takes full advantage of the merits of an ideal sliding mode, fuzzy rules, and multiresolution WNN. The fuzzy algorithm is used to dynamically adjust the weights of the WNN and effectively accelerate the convergence rate. In addition, the DC controller is designed to greatly decrease the effect of the approximation error and guarantee the system stability in the sense of the Lyapunov theory. Finally, the proposed algorithms are carried out on the semiphysical simulation platform, the precision and superiority of which are comparatively verified based on the simulation results. |
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AbstractList | A fuzzy multiresolution wavelet neural network (FMWNN) controller with dynamic compensation (DC) is proposed to address the complexities of the electric load simulator (ELS). The FMWNN acts as a main torque tracking controller, which takes full advantage of the merits of an ideal sliding mode, fuzzy rules, and multiresolution WNN. The fuzzy algorithm is used to dynamically adjust the weights of the WNN and effectively accelerate the convergence rate. In addition, the DC controller is designed to greatly decrease the effect of the approximation error and guarantee the system stability in the sense of the Lyapunov theory. Finally, the proposed algorithms are carried out on the semiphysical simulation platform, the precision and superiority of which are comparatively verified based on the simulation results. |
Audience | Academic |
Author | Liu, Rongzhong Gao, Qiang Wang, Chao Hou, Runmin Hou, Yuan-long |
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Cites_doi | 10.1016/j.neucom.2014.01.038 10.1016/s1000-9361(09)60275-2 10.1109/TSMC.1985.6313399 10.1016/j.ijnonlinmec.2009.08.009 10.1016/j.eswa.2011.08.052 10.1016/j.cja.2013.02.026 10.1016/j.cja.2013.04.001 10.1016/j.asoc.2012.11.029 10.1109/72.80202 10.1016/j.neucom.2013.03.001 10.1109/78.388860 10.1080/0020772031000081982 10.1016/j.engappai.2012.03.018 10.1016/j.asoc.2014.02.027 10.1002/aic.690390108 10.1016/s1000-9361(11)60201-x 10.1016/j.isatra.2013.01.004 10.1016/j.mechatronics.2013.11.005 10.1016/j.eswa.2006.10.002 10.1016/j.cnsns.2010.12.011 10.1002/asjc.881 10.1016/j.engappai.2012.05.003 10.1002/asjc.986 10.1002/asjc.824 10.3724/sp.j.1004.2010.00573 10.1016/j.neucom.2011.10.035 10.1016/j.neunet.2012.03.005 10.1016/S1000-9361(11)60467-6 10.1002/asjc.945 10.1109/72.165591 10.1109/91.481843 10.1016/j.neucom.2013.03.065 10.1007/s12206-012-0879-4 10.1016/j.eswa.2012.02.172 10.1002/asjc.989 |
ContentType | Journal Article |
Copyright | Copyright © 2016 Chao Wang et al. COPYRIGHT 2016 John Wiley & Sons, Inc. Copyright © 2016 Chao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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SubjectTerms | Algorithms Compensation Computer simulation Control algorithms Control systems Controllers Fault tolerance Firearm laws & regulations Fuzzy Fuzzy logic Guarantees Loads (forces) Neural networks Systems stability |
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Title | Control of the Electric Load Simulator Using Fuzzy Multiresolution Wavelet Neural Network with Dynamic Compensation |
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