A design study of 4/2 switched reluctance motor using particle swarm optimization

2012 ◽  
Author(s):  
Winna Phuangmalai ◽  
Mongkol Konghirun ◽  
Nattapon Chayopitak
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Switched Reluctance Motor ◽  
Design Study ◽  
Swarm Optimization ◽  
Switched Reluctance ◽  
Reluctance Motor

Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

2019 ◽  
Vol 41 (14) ◽  
pp. 4114-4128 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Switched Reluctance Motor ◽  
Drive System ◽  
Swarm Optimization ◽  
Switched Reluctance ◽  
Adaptive Law ◽  
Reluctance Motor ◽  
Nonlinear Backstepping Control

A switched reluctance motor (SRM) drive system has highly nonlinear uncertainties owing to a convex construction. It is hard for the linear control methods to achieve good performance for the SRM drive system. An adaptive nonlinear backstepping control system using the mended recurrent Romanovski polynomials neural network and mended PSO with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SRM drive system. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the mended recurrent Romanovski polynomials neural network and the error estimated law are established. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the two varied learning rates of the two parameters in the mended recurrent Romanovski polynomials neural network. Finally, some experimental results and a comparative analysis are verified that the proposed control scheme has better control performances for controlling the SRM drive system.

Particle Swarm Optimization Method to Design a Linear Tubular Switched Reluctance Generator

2015 ◽  
pp. 469-506
Author(s):  
Rui P. G. Mendes ◽  
Maria do Rosário Alves Calado ◽  
Sílvio José Mariano
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Method ◽  
Relative Displacement ◽  
Rate Of Change ◽  
Swarm Optimization ◽  
Switched Reluctance ◽  
Electric Generation ◽  
Particle Swarm Optimization Method ◽  
Structural Configurations

In this chapter, the Particle Swarm Optimization method is applied to four different structural configurations of a linear switched reluctance generator with tubular topology. The optimization process involves the search of the values for a defined set of geometric parameters that maximize the rate of change of the generator's inductance with the relative displacement of its mover part. The optimization algorithm is applied to each structural configuration in order to find the optimum geometry as well to identify the most suitable configuration for electric generation.

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