The design parameters and methods

Model
The case of the motor is 3, 6 very quiet incentive sons, and 4 very rotors of switched reluctance motor (SRM). Control system simulation in Simulink is established in, motor in the calculation in the electromagnetic field ANSYS, inverter circuits in the PSpice modeling. (figure 2)
Figure 2-motor control system model of Simulink
Selection of design parameters
Three design parameters were motor poles wide, excitation signal starting angle and the width of the excitation signal.

Optimization goal is to

rotating joint minimize the output torque ripple of the motor. Constraints is greater than 1000 RPM speed motors.
Methods
Experimental design and the response surface model
Experimental design (DOE) method and the response surface model (RSM) is used to explore design space. In this case, the

application of the 100 samples points of Latin super cubic method. On this basis, based on the least squares Taylor polynomial,

the response surface fitting experimental design to sample points.
Design optimization
OPTIMUS in this case the application of adaptive evolution (SAE) Genetic Algorithm, based in response to
rotary union find the solution on

the smallest motor output torque ripple, also satisfy motor speed not less than 1000 RPM. In response to
fiber optic rotary joint the surface for the

optimal solution, and in the coming in simulation workflow solution of the local optimization process, is used as a starting

point. So, through several optimization algorithm, different ways of solving the combination strategy, make finally able to find

the optimal design, while reducing the optimization process of time.
Figure 3-contributions graph shows the magnetic poles wide and excitation signal motor starting point of the output torque

ripple is the biggest impact on design parameters