Kadir ERKAN, Murat ATLIHAN, Barış Can YALÇIN

Force and torque parameter estimation for a 4-pole hybrid electromagnet by ANFIS hybrid learning algorithm

In this study, a force and torque estimation method based on an adaptive neuro-fuzzy inference system (ANFIS) has been developed to get rid of multiple integral calculations of air gap coefficients that cause time delay for magnetic levitation control applications. During magnetic levitation applications that contain a 4-pole hybrid electromag- net, multiple integral calculations have to be done for obtaining air gap permanence parameters, and these parameters are needed to calculate force and torque parameters that are produced by the poles of the hybrid electromagnet, which means, if time delay occurs for calculation of permanence parameters, actual force and actual torque values that are produced by hybrid electromagnet's poles cannot be exactly known; thus, the advantage of having an exact model of the system gets lost and, as a result, the controller's performance goes down. To address a solution, an ANFIS using a hybrid learning algorithm consisting of backpropagation and least-squares learning methods is proposed to estimate force and torque parameters using training data already obtained using multiple integral calculations before.

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[1] Knospe CR. Active magnetic bearings for machining applications. Con Eng Prac 2007; 15: 307-313.
[2] Yang G, Shi Z, Mo N, Zhao L. Research on active magnetic bearing applied in Chinese modular high-temperature gas-cooled reactor : Prog Nuc Eng 2014; 77: 352-360.
[3] Lin FJ, Chen SY, Huang MS. Adaptive complementary sliding-mode control for thrust active magnetic bearing system. Con Eng Prac 2011; 19: 711-722.
[4] Tang J, Sun J, Fang J, Sam GS. Low eddy loss axial hybrid magnetic bearing with gimballing control ability for momentum ywheel. J Mag Mag Mat 2013; 329: 153-164.
[5] Okano M, Iwamoto T, Fuchino S, Tamada N. Feasibility of a goods transportation system with a superconducting magnetic levitation guide { load characteristics of a magnetic levitation guide using a bulk high-Tc superconductor. Phy C: Supercon 2003; 386: 500-505.
[6] D'Ovidio G, Crisi F, Lanzara G. A V" shaped superconducting levitation module for lift and guidance of a magnetic transportation system. Phy C: Supercon 2008; 468: 1036-1040.
[7] Shu QS, Cheng G, Susta JT, Hull JR, Fesmire JE, Augustanowicz SD, Demko JA, Werfel FN. Magnetic levitation technology and its applications in exploration projects. Cryo 2006; 46: 105-110.
[8] Sari I, Kraft M. A MEMS linear accelerator for levitated micro-objects. Sens Act A: Phy 2015; 222: 15-23
[9] Kim WJ, Verma S, Shakir H. Design and precision construction of novel magnetic-levitation-based multi-axis nanoscale positioning systems. Pre Eng 2007; 31: 337-350.
[10] van West E, Yamamoto A, Higuchi T. Automatic object release in magnetic and electrostatic levitation systems. Pre Eng 2009; 33: 217-228.
[11] Erkan K, Acarkan B, Koseki T. Zero-power levitation control design for a 4-pole electromagnet on the basis of a transfer function approach. In Elec Mac & D Conf, 2007. IEMDC '07. IEEE Int. 2007.
[12] Bachle T, Hentzelt S, Graichen K. Nonlinear model predictive control of a magnetic levitation system. Con Eng Prac 2013; 21: 1250-1258.
[13] Yang JH, Lee YS, Kwon OK. Development of magnetic force modeling equipment for magnetic levitation system. In Con Auto and Sys (ICCAS), 2010.