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Development of ARX Models for predictive control using fractional order and orthonormal basis filter parametrization / Muddu Madakyaru in Industrial & engineering chemistry research, Vol. 48 N° 19 (Octobre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 19 (Octobre 2009) . - pp. 8966–8979 Titre : Development of ARX Models for predictive control using fractional order and orthonormal basis filter parametrization Type de document : texte imprimé Auteurs : Muddu Madakyaru, Auteur ; Anuj Narang, Auteur ; Sachin C. Patwardhan, Auteur Année de publication : 2009 Article en page(s) : pp. 8966–8979 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Model  predictive  control  ARX  models  Fractional-order  differential  operators  Orthonormal  basis  filters Résumé : Among various industrial vendors of model predictive control (MPC) schemes, ARX appears to be a popular choice of model structure while models for predictive control schemes are being developed. These models are, however, nonparsimonious in the number of model parameters. As a consequence, the length of the data required to keep the variance errors low while using the conventional ARX structure is significantly large. Thus, if it is possible to reparametrize the ARX model such that fewer parameters are required at the identification stage, then it is possible to reduce the length of identification data and thereby reduce the cost involved in model identification exercise. In this work, we explore the possibility of reparametrizing ARX models using the fractional-order differential operators (FO-ARX) and orthonormal basis filters (OBF-ARX). We also propose a novel approach for identification of time delay matrix from multivariate data using ARX and OBF-ARX models. The efficacy of the proposed modeling technique is demonstrated by conducting simulation studies on the benchmark Shell control problem. Analysis of the simulation results reveals that, when compared with the conventional high-order ARX structure, the FO-ARX and the OBF-ARX are better model parametrizations when the data length is less. In particular, OBF-ARX parametrization is able to estimate the time delay matrix from multivariate data quite accurately. The experimental studies establish the feasibility of using the proposed FO-ARX and OBF-ARX models for formulating MPC schemes. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009439 [article] Development of ARX Models for predictive control using fractional order and orthonormal basis filter parametrization [texte imprimé] / Muddu Madakyaru, Auteur ; Anuj Narang, Auteur ; Sachin C. Patwardhan, Auteur . - 2009 . - pp. 8966–8979. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 19 (Octobre 2009) . - pp. 8966–8979 Mots-clés : Model  predictive  control  ARX  models  Fractional-order  differential  operators  Orthonormal  basis  filters Résumé : Among various industrial vendors of model predictive control (MPC) schemes, ARX appears to be a popular choice of model structure while models for predictive control schemes are being developed. These models are, however, nonparsimonious in the number of model parameters. As a consequence, the length of the data required to keep the variance errors low while using the conventional ARX structure is significantly large. Thus, if it is possible to reparametrize the ARX model such that fewer parameters are required at the identification stage, then it is possible to reduce the length of identification data and thereby reduce the cost involved in model identification exercise. In this work, we explore the possibility of reparametrizing ARX models using the fractional-order differential operators (FO-ARX) and orthonormal basis filters (OBF-ARX). We also propose a novel approach for identification of time delay matrix from multivariate data using ARX and OBF-ARX models. The efficacy of the proposed modeling technique is demonstrated by conducting simulation studies on the benchmark Shell control problem. Analysis of the simulation results reveals that, when compared with the conventional high-order ARX structure, the FO-ARX and the OBF-ARX are better model parametrizations when the data length is less. In particular, OBF-ARX parametrization is able to estimate the time delay matrix from multivariate data quite accurately. The experimental studies establish the feasibility of using the proposed FO-ARX and OBF-ARX models for formulating MPC schemes. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009439