Optimizing the Process Parameters of GMV Controller by PSO Tuning Method
System modeling is very important to develop a mathematical model that describes the dynamics of a system. This work proposes a modeling and designing a Generalized Minimum Variance (GMV) controller using self-tuning and Particle Swarm Optimization (PSO) tuning method for a hot air blower system. Pa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/11433/1/Optimizing_the_Process_Parameters_of_GMV_Controllers_by_PSO_Tuning_Method.pdf http://eprints.utem.edu.my/id/eprint/11433/ |
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| Summary: | System modeling is very important to develop a mathematical model that describes the dynamics of a system. This work proposes a modeling and designing a Generalized Minimum Variance (GMV) controller using self-tuning and Particle Swarm Optimization (PSO) tuning method for a hot air blower system. Parametric approach using AutoRegressive with Exogenous input (ARX) model structure is used to estimate the approximated model plant. The approximated plant model is then being estimated using System Identification approach. The results based on simulation using MATLAB shows that the GMV controller using PSO tuning method offers a reasonable tracking performances of the system’s output. |
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