INTEGRATION OF PARTICLE SWARM OPTIMIZATION (PSO) TECHNIQUE INTO DC MOTOR CONTROL
Particle Swarm Optimization (PSO), an artificial method to determine the optimal proportional- integral- derivative (PID) controller parameters to be integrated into a brushed DC motor is presented. Particle Swarm Optimization (PSO), developed by Eberhart and Kennedy in 1995 was inspired by swarm...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Universiti Teknologi PETRONAS
2011
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| Online Access: | http://utpedia.utp.edu.my/10516/1/2011%20-%20Integration%20of%20Particle%20Swarm%20Optimization%20%28PSO%29%20Technique%20into%20DC%20Motor%20Control.pdf http://utpedia.utp.edu.my/10516/ |
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| Summary: | Particle Swarm Optimization (PSO), an artificial method to determine the optimal
proportional- integral- derivative (PID) controller parameters to be integrated into a
brushed DC motor is presented. Particle Swarm Optimization (PSO), developed by
Eberhart and Kennedy in 1995 was inspired by swarming patterns occurring in
nature such as flocking birds. It was observed that each individual exchanges
previous experience, hence knowledge of the "best position" attained by an
individual becomes globally known. In the study, the problem of identifying the PID
controller parameters is considered as an optimization problem. An attempt has been
made to determine the PID parameters employing the PSO technique. This technique
is used to improve the step response of a second order system. The step response of
the given system is defined in rise time, settling time and peak overshoot. The best
parameters to be used for PSO that can optimize the performance of a DC Motor
(e.g.: population size, acceleration constant and inertia weight factor) is evaluated.
First chapter discusses the types of DC motor available in industry nowadays and the
origination of Particle Swarm Optimization technique itself. Next, the following
chapter continues with the implementation of DC motor control and the tuning
available that has been researched before. The usage of Particle Swarm Optimization
technique is briefly explained which comprises the 6-steps of selection process. For
this study, the software used is MATLAB/Simulink, where the implementation of the
chosen DC motor model is represented and Particle Swarm Optimization is
integrated into the PID controller of the motor, to observe the performance of chosen
parameters. The results of PID controller tuning and also the results for the
implementation ofPSO based PID controller is presented on the Result & Discussion
chapter. Comparison then is made and discussed to see whether the results are as
expected. Lastly, recommendation and conclusion pertaining to the completion of
this project is presented. |
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