Improved Tracking Performances Of A Hot Air Blower System Using Generalized Minimum Variance (GMV) Controller With Particle Swarm Optimization (PSO) And Harmony Search Algorithm (HSA) Tuning Method

Lim, Hooi Chen (2015) Improved Tracking Performances Of A Hot Air Blower System Using Generalized Minimum Variance (GMV) Controller With Particle Swarm Optimization (PSO) And Harmony Search Algorithm (HSA) Tuning Method. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text (24 Pages) Improved Tracking Performances Of A Hot Air Blower System Using Generalized Minimum Variance (GMV) Controller With Particle Swarm Optimization (PSO) And Harmony Search Algorithm (HSA) Tuning Method.pdf - Submitted Version Download (610kB)

Abstract

Hot air blower system is the process of heating of the air flowing in the tube up to the desired temperature level. The crucial part that can be seen from this system is to control the temperature of a flowing air. In this project, a PT326 process trainer, which is a hot air blower system is used. This project is conducted due to this problem. The scope of work for this research include modelling and controller design of a PT326 process trainer. Generalized minimum variance (GMV) controller is designed with MATLAB software to control the purpose of maintaining the process temperature at a desired value. The simulation result aim to make a comparison of the performances of the process temperature when using particle swarm optimization (PSO) and Harmony Search Algorithm (HSA). Through simulation, the performances of the hot air blower system with the use of GMV controller with PSO tuning method is better than HSA.

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