GABP-based multiparameter identification for permanent magnet synchronous linear motors

Permanent magnet synchronous linear motor (PMSLM) parameters are susceptible to temperature changes and motor aging, which can result in inaccurate models and reduced control accuracy. Therefore, PMSLM parameters must be monitored accurately. In this study, a back-propagation (BP) neural network is used to identify the resistance, inductance, and flux linkage parameters of a PMSLM, and a genetic algorithm (GA) is employed to optimize the identification system. A full-rank model is established, and the BP neural network parameter identifier is used to realize the multiparameter discrimination, and the GA is employed to optimize the network parameters of the identifier model to improve its training speed and accuracy. A GABP-based multiparameter identification system for a PMSLM is built, and the experiment results show that the GABP parameter identifier can accurately identify the four parameter values of the PMSLM simultaneously.