mathematical modeling and pid control of brushless dc motor
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mathematical modeling and pid control of brushless dc motor

Views: 0     Author: Site Editor     Publish Time: 2020-10-29      Origin: Site

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The basic principle of the Brushless DC Motor: The rotor of the brushless DC motor has a permanent magnet and the stator has a winding.
It is essentially a DC motor that rotates from the inside out.
The brush and the diverter have been eliminated and the winding is connected to the control electronics.
Control the function of the electronic device to replace the converter and power up the appropriate winding.
As shown in the figure.
1. the winding is powered on in a pattern rotating around the stator.
The energized stator winding guides the rotor magnet and switches when the rotor is aligned with the stator I. e.
The rotor magnetic field chase the rotating stator magnetic field and never catch up.
No PID controller performance: once the transfer function is known, the next step is to check the parameters of the motor by applying the step input to the motor using MATLAB code.
The MATLAB code and the obtained parameters and the performance of the PID controller are as follows: the system is stable, because the root trajectory of the pole is on the left half plane, but the parameters of the system are not expected at all, so a PID controller is needed.
Therefore, a controller is designed with its gain I. e. (kp, ki ,kd)
Adjusted using the PID tuner application in MATLAB to optimize the performance of the system, the MATLAB code for step response, performance parameters and root trajectory is given below.
Results comparison: the values of kp, ki and kd optimized by the system are given in the table. 3.
The table compares the result values with and without the parameters of the PID controller. 4.
Conclusion: there is no PID controller, system I. e.
The response of the BLDC motor to the stepping input is very poor.
It has a high settling and rising time.
After using the PID tuner application in MATLAB to introduce the PID into the system, the system becomes more stable as its root trajectory changes and the response to the step input is optimized.
This shows how important PID is in the control system.

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