Brushless DC(BLDC)motors are widely used in Electric Vehicle applications because ofrntheir high starting torque, high efficiency, long operating life and better speed versus torquerncharacteristics. The major problem in the brushless dc motor drive system is that somerndisturbances originate in the drive which will result in errors and reduce the stability of thernsystem. This problem can be fixed by using good modeling approach and high performancerncontrollers like linear quadratic gaussian. But the selection or tuning of the parameters ofrnthe linear quadratic gaussian controller is a tedious process. Therefore it is important tornuse artificial intelligence based optimization methods to select the parameters of the linearrnquadratic gaussian controllers to achieve the high performance of linear quadratic gaussianrncontroller.rnHence, in this thesis, a linear quadratic gaussian controller tuned with PSO is designed andrnitâ€™s performance in speed control for a Brushless DC motor is analyzed. The performance ofrnthe proposed controller of brushless dc motor was analyzed in terms of speed tracking capability,rnback emf and hall sensor response, high and low-speed behavior, and speed reversalrnconditions using MATLAB /SIMULINK.rnThe linear quadratic gaussian controller performance has been compared with proportionalrnintegral control strategies in terms of the four quadrants operation and braking systemrnresponse.The simulation results show that the linear quadratic gaussian-particle swarm optimizationrncontroller has a more significant overshoot reduction compared to PI controllers,andrna good transient response with a rise time , settling time,and the minimum steady-state speedrnerror percentage of has been achieved for linear quadratic gaussian-particle swarm optimization.rnThen particle swarm optimization tuned linear quadratic gaussian controller methodsrnare better when compared with PI conventional methods.