Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the parameters of PSRMs is one critical issues that needs be urgently solved. Therefore, a nonlinear dynamic model-based method proposed in this paper. First, improve accuracy motor dynamics model, Hammerstein–Wiener model based on BP neural network input–output module established by combining linear and structures so characteristics system characterized simultaneously. Then, developed using model. In addition, with self-designed simulated annealing adaptive particle swarm algorithm (SAAPSO), performed offline iteratively obtain optimal parameters. Simulations experiments carried out corresponding results show obtained can directly applied actual performance improved