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 po...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-10, Vol.11 (19), p.4067
Main Authors: Huang, Su-Dan, Lin, Zhixiang, Cao, Guang-Zhong, Liu, Ningpeng, Mou, Hongda, Xu, Junqi
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Analysis
Annealing
Back propagation networks
Computer simulation
Control systems
Dynamic models
Dynamical systems
Efficiency
Electric motors
Hammerstein–Wiener model
Mathematical optimization
Methods
Neural networks
nonlinear dynamic model
Nonlinear dynamics
Optimal control
Optimization algorithms
Parameters
Particle swarm optimization
particle swarm optimization algorithm
planar switched reluctance motor
Reluctance
Simulated annealing
simulated annealing algorithm
Systems stability
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11194067