Linear Quadratic Gaussian Controller Tuned With Particle Swarm Optimization For Speed Tracking Control of Brushless DC Motor
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Date
2022-02
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Addis Ababa University
Abstract
Brushless DC(BLDC)motors are widely used in Electric Vehicle applications because of
their high starting torque, high efficiency, long operating life and better speed versus torque
characteristics. The major problem in the brushless dc motor drive system is that some
disturbances originate in the drive which will result in errors and reduce the stability of the
system. This problem can be fixed by using good modeling approach and high performance
controllers like linear quadratic gaussian. But the selection or tuning of the parameters of
the linear quadratic gaussian controller is a tedious process. Therefore it is important to
use artificial intelligence based optimization methods to select the parameters of the linear
quadratic gaussian controllers to achieve the high performance of linear quadratic gaussian
controller.
Hence, in this thesis, a linear quadratic gaussian controller tuned with PSO is designed and
it’s performance in speed control for a Brushless DC motor is analyzed. The performance of
the proposed controller of brushless dc motor was analyzed in terms of speed tracking capability,
back emf and hall sensor response, high and low-speed behavior, and speed reversal
conditions using MATLAB /SIMULINK.
The linear quadratic gaussian controller performance has been compared with proportional
integral control strategies in terms of the four quadrants operation and braking system
response.The simulation results show that the linear quadratic gaussian-particle swarm optimization
controller has a more significant overshoot reduction compared to PI controllers,and
a good transient response with a rise time , settling time,and the minimum steady-state speed
error percentage of has been achieved for linear quadratic gaussian-particle swarm optimization.
Then particle swarm optimization tuned linear quadratic gaussian controller methods
are better when compared with PI conventional methods.
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Keywords
LQG- PSO, Breaking, Brushless DC Motor, Electrical Vehicle