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Batch-to-Batch Iterative Learning Control of a Batch Polymerization Process Based on Online Sequential Extreme Learning Machine / Tang Ao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11108–11114 Titre : Batch-to-Batch Iterative Learning Control of a Batch Polymerization Process Based on Online Sequential Extreme Learning Machine Type de document : texte imprimé Auteurs : Tang Ao, Auteur ; Xiao Dong, Auteur ; Mao Zhizhong, Auteur Année de publication : 2010 Article en page(s) : pp. 11108–11114 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Batch-to-Batch--Iterative--Learning--Control--Batch--Polymerization--Process--Based--Online--Sequential--Extreme--Learning--Machine Résumé : This paper develops a batch-to-batch iterative learning control (ILC) strategy based on online sequential extreme learning machine (OS-ELM) for batch optimal control. On the basis of extreme learning machine (ELM), a data-based nonlinear model is first adopted to capture the batch process characteristics aiming to obtain superior predictive accuracy. Subsequently, due to the model−plant mismatch in real batch processes, an ILC algorithm with adjusting input trajectory by means of error feedback is employed focusing on the improvement of the final product quality. In order to cope with the problems of the unknown disturbances and process variations from batch to batch, when a batch run is completed, OS-ELM is utilized to update the model weights so as to guarantee the precision of the model for optimal control, which corresponds to a nonlinear updating procedure. The feasibility and effectiveness of the proposed method are demonstrated via the application to a simulated bulk polymerization of the styrene batch process, and the simulation results show superior performance. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9007979 [article] Batch-to-Batch Iterative Learning Control of a Batch Polymerization Process Based on Online Sequential Extreme Learning Machine [texte imprimé] / Tang Ao, Auteur ; Xiao Dong, Auteur ; Mao Zhizhong, Auteur . - 2010 . - pp. 11108–11114. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11108–11114 Mots-clés : Batch-to-Batch--Iterative--Learning--Control--Batch--Polymerization--Process--Based--Online--Sequential--Extreme--Learning--Machine Résumé : This paper develops a batch-to-batch iterative learning control (ILC) strategy based on online sequential extreme learning machine (OS-ELM) for batch optimal control. On the basis of extreme learning machine (ELM), a data-based nonlinear model is first adopted to capture the batch process characteristics aiming to obtain superior predictive accuracy. Subsequently, due to the model−plant mismatch in real batch processes, an ILC algorithm with adjusting input trajectory by means of error feedback is employed focusing on the improvement of the final product quality. In order to cope with the problems of the unknown disturbances and process variations from batch to batch, when a batch run is completed, OS-ELM is utilized to update the model weights so as to guarantee the precision of the model for optimal control, which corresponds to a nonlinear updating procedure. The feasibility and effectiveness of the proposed method are demonstrated via the application to a simulated bulk polymerization of the styrene batch process, and the simulation results show superior performance. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9007979

Batch-to-batch iterative learning control of a batch polymerization process based on online sequential extreme learning machine / Tang Ao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11108–11114 Titre : Batch-to-batch iterative learning control of a batch polymerization process based on online sequential extreme learning machine Type de document : texte imprimé Auteurs : Tang Ao, Auteur ; Xiao Dong, Auteur ; Mao Zhizhong, Auteur Année de publication : 2010 Article en page(s) : pp. 11108–11114 Note générale : Chemical engineeringg Langues : Anglais (eng) Mots-clés : Batch-to-batch  iterative  learning  control  Online  sequential  extreme  learning  machine  Extreme  learning  machine Résumé : This paper develops a batch-to-batch iterative learning control (ILC) strategy based on online sequential extreme learning machine (OS-ELM) for batch optimal control. On the basis of extreme learning machine (ELM), a data-based nonlinear model is first adopted to capture the batch process characteristics aiming to obtain superior predictive accuracy. Subsequently, due to the model−plant mismatch in real batch processes, an ILC algorithm with adjusting input trajectory by means of error feedback is employed focusing on the improvement of the final product quality. In order to cope with the problems of the unknown disturbances and process variations from batch to batch, when a batch run is completed, OS-ELM is utilized to update the model weights so as to guarantee the precision of the model for optimal control, which corresponds to a nonlinear updating procedure. The feasibility and effectiveness of the proposed method are demonstrated via the application to a simulated bulk polymerization of the styrene batch process, and the simulation results show superior performance. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9007979 [article] Batch-to-batch iterative learning control of a batch polymerization process based on online sequential extreme learning machine [texte imprimé] / Tang Ao, Auteur ; Xiao Dong, Auteur ; Mao Zhizhong, Auteur . - 2010 . - pp. 11108–11114. Chemical engineeringg Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11108–11114 Mots-clés : Batch-to-batch  iterative  learning  control  Online  sequential  extreme  learning  machine  Extreme  learning  machine Résumé : This paper develops a batch-to-batch iterative learning control (ILC) strategy based on online sequential extreme learning machine (OS-ELM) for batch optimal control. On the basis of extreme learning machine (ELM), a data-based nonlinear model is first adopted to capture the batch process characteristics aiming to obtain superior predictive accuracy. Subsequently, due to the model−plant mismatch in real batch processes, an ILC algorithm with adjusting input trajectory by means of error feedback is employed focusing on the improvement of the final product quality. In order to cope with the problems of the unknown disturbances and process variations from batch to batch, when a batch run is completed, OS-ELM is utilized to update the model weights so as to guarantee the precision of the model for optimal control, which corresponds to a nonlinear updating procedure. The feasibility and effectiveness of the proposed method are demonstrated via the application to a simulated bulk polymerization of the styrene batch process, and the simulation results show superior performance. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9007979