Parameter identification of a Novel Controllable Excitation Feeding Platform Based on Improved Particle Swarm Optimization

High-precision parameters on the linear synchronous motor of a new magnetic suspension feed platform obtained become a crucial problem of improving accuracy of a controller. The parameters by some regular programs measured are limited. Considering the advantages and disadvantages of differential evolution algorithm and basic particle swarm optimization algorithm, an intelligent evolutionary algorithm based on natural-selective learning-based differential mutation is proposed to optimize particle swarm optimization. The proposed method is applied to the parameter identification and modeling of the platform feed system, and a multi-parameter identification model of linear synchronous motor is constructed based on the particle swarm optimization algorithm, which is improved from elite learning differential evolution. The experimental results show that the proposed algorithm has higher accuracy, the lowest error reaches 0.3%, and the convergence performance is more than doubled compared with the traditional improved particle swarm optimization algorithm.