Position sensorless control of switched reluctance motor for shearer traction system based on RBF neural network
Abstract：Feasibility of using sensorless SRM for shearer traction system was proposed.Nonlinear characteristics of SRM and its real-time calculation method were analyzed.Radial basis function（RBF） neural network of rotor position estimation for sensorless SRM drive was established,with two input variables：flux linkage and phase current.Real-time rotor position angle obtained from shaft encoder was adopted as learning sample data,on-line learning algorithms and training procedures were also given.Sensorless control of SRM based on RBF neural network was achieved by TMS320F2812 DSP,18.5 kW three-phase 12/8 pole sensorless controller was set up.Traction experimental results of shearer show that the system has a good dynamic performance and high accuracy position detection with maximum error less than 2°.
Keywords：radial basis function（RBF）sensorlessswitched reluctance motor（SRM）shearer traction system
Publication Date：2011-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)