Particle swarm optimization based tuning of a modified smith predictor for mould level control in continuous casting

Autor: Emmanuel Godoy, Bertrand Bèle, Didier Dumur, Karim Jabri, Alain Mouchette
Přispěvatelé: Supélec Sciences des Systèmes (E3S), Ecole Supérieure d'Electricité - SUPELEC (FRANCE), ArcelorMittal Maizières Research SA, ArcelorMittal, Supélec Sciences des Systèmes [Gif-sur-Yvette] (E3S), SUPELEC, Dartron, Josiane
Jazyk: angličtina
Rok vydání: 2011
Předmět:
0209 industrial biotechnology
Engineering
Mathematical optimization
Control (management)
0102 computer and information sciences
02 engineering and technology
01 natural sciences
Industrial and Manufacturing Engineering
[SPI.AUTO]Engineering Sciences [physics]/Automatic
Reduction (complexity)
Setpoint
020901 industrial engineering & automation
Robustness (computer science)
Control theory
0202 electrical engineering
electronic engineering
information engineering
Multi-swarm optimization
ComputingMilieux_MISCELLANEOUS
business.industry
Process (computing)
Particle swarm optimization
General Medicine
Computer Science Applications
Smith predictor
Continuous casting
[SPI.AUTO] Engineering Sciences [physics]/Automatic
010201 computation theory & mathematics
Control and Systems Engineering
Modeling and Simulation
Structure based
020201 artificial intelligence & image processing
business
Zdroj: Journal of Process Control
Journal of Process Control, Elsevier, 2011, 21 (2), pp.263-270
Proceedings on IFAC Workshop Automation in Mining, Mineral and Metal Industry
Workshop Automation in Mining, Mineral and Metal Industry
Workshop Automation in Mining, Mineral and Metal Industry, 2009, Viña del Mar, Chile. pp.CD-Rom Proceedings
ISSN: 0959-1524
Popis: Mould level variations are a serious productivity and quality problem in continuous casting process. This work proposes a level control structure based on the Astrom's modified Smith predictor able to improve the bulging effect rejection. Unlike conventional methods, this control strategy decouples the disturbance rejection from the setpoint response and therefore can be independently optimized. Using this scheme, the bulging rejection specifications are reformulated as a H∞ problem and the tuning parameters are designed through the particle swarm optimization approach. Simulation results confirm that the proposed architecture is more effective than the other ones currently implemented in real plants.
Databáze: OpenAIRE
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