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

Automated summary

This paper proposes a nonlinear dynamic model-based position control parameter optimization method for planar switched reluctance motors (PSRMs). By establishing a Hammerstein-Wiener model based on the input-output nonlinear module of BP neural network, the accuracy of the motor dynamics model is improved. The position control parameters are optimized using a simulated annealing adaptive particle swarm optimization algorithm (SAAPSO). Simulation and experimental results demonstrate that the proposed method effectively improves the control performance of PSRMs.