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Published on: August 12, 2013
Nonlinear system modeling based on bilinear Laguerre orthonormal bases
Tarek Garna1, Kais Bouzrara, José Ragot
1Unité de Recherche Automatique, Traitement de Signal et d'Image, Ecole Nationale d'Ingénieurs de Monastir, Rue Ibn Eljazzar, 5019 Monastir, Tunisia. tarek.garna@enim.rnu.tn
ISA Transactions
|January 8, 2013
Summary
This study introduces a novel bilinear-Laguerre model for system representation, significantly reducing parameters. An optimization algorithm enhances model accuracy, validated on a Continuous Stirred Tank Reactor (CSTR) system.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Mathematical Modeling
Background:
- Classical bilinear models often require a large number of parameters.
- Efficient representation and parameter reduction are crucial for complex system modeling.
Purpose of the Study:
- To propose a new discrete bilinear model representation using Laguerre orthonormal bases.
- To develop a pole optimization algorithm for improved model performance.
Main Methods:
- Developing coefficients for input, output, and cross-product terms on Laguerre bases.
- Extending an existing pole optimization algorithm (Tanguy et al.) for Laguerre pole selection.
- Simulating and validating the bilinear-Laguerre model on a Continuous Stirred Tank Reactor (CSTR) system.
Main Results:
- The proposed bilinear-Laguerre model achieves significant parameter reduction compared to classical models.
- The model offers a simpler recursive representation.
- The pole optimization algorithm effectively determines optimal Laguerre poles.
Conclusions:
- The bilinear-Laguerre model provides an efficient and reduced-parameter alternative for discrete bilinear system representation.
- The developed pole optimization algorithm is effective for enhancing model accuracy.
- The model and algorithm are validated on a practical CSTR system.
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