Documents disponibles écrits par cet auteur

Constrained nonlinear state estimation using ensemble kalman filters / J. Prakash in Industrial & engineering chemistry research, Vol. 49 N° 5 (Mars 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2242–2253 Titre : Constrained nonlinear state estimation using ensemble kalman filters Type de document : texte imprimé Auteurs : J. Prakash, Auteur ; Sachin C. Patwardhan, Auteur ; Shah, Sirish L., Auteur Année de publication : 2010 Article en page(s) : pp. 2242–2253 Note générale : Industrial Chemistry Langues : Anglais (eng) Mots-clés : EnKF;  kalman Résumé : Recursive estimation of states of constrained nonlinear dynamic systems has attracted the attention of many researchers in recent years. In this work, we propose a constrained recursive formulation of the ensemble Kalman filter (EnKF) that retains the advantages of the unconstrained EnKF while systematically dealing with bounds on the estimated states. The EnKF belongs to the class of particle filters that are increasingly being used for solving state estimation problems associated with nonlinear systems. A highlight of our approach is the use of truncated multivariate distributions for systematically solving the estimation problem in the presence of state constraints. The efficacy of the proposed constrained state estimation algorithm using the EnKF is illustrated by application on two benchmark problems in the literature (a simulated gas-phase reactor and an isothermal batch reactor) involving constraints on estimated state variables and another example problem, which involves constraints on the process noise. Note de contenu : Bibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900197s [article] Constrained nonlinear state estimation using ensemble kalman filters [texte imprimé] / J. Prakash, Auteur ; Sachin C. Patwardhan, Auteur ; Shah, Sirish L., Auteur . - 2010 . - pp. 2242–2253. Industrial Chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2242–2253 Mots-clés : EnKF;  kalman Résumé : Recursive estimation of states of constrained nonlinear dynamic systems has attracted the attention of many researchers in recent years. In this work, we propose a constrained recursive formulation of the ensemble Kalman filter (EnKF) that retains the advantages of the unconstrained EnKF while systematically dealing with bounds on the estimated states. The EnKF belongs to the class of particle filters that are increasingly being used for solving state estimation problems associated with nonlinear systems. A highlight of our approach is the use of truncated multivariate distributions for systematically solving the estimation problem in the presence of state constraints. The efficacy of the proposed constrained state estimation algorithm using the EnKF is illustrated by application on two benchmark problems in the literature (a simulated gas-phase reactor and an isothermal batch reactor) involving constraints on estimated state variables and another example problem, which involves constraints on the process noise. Note de contenu : Bibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900197s

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

Development of a closed form nonlinear predictive control law based on a class of wiener models / Shraddha Deshpande in Industrial & engineering chemistry research, Vol. 49 N° 1 (Janvier 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 148–165 Titre : Development of a closed form nonlinear predictive control law based on a class of wiener models Type de document : texte imprimé Auteurs : Shraddha Deshpande, Auteur ; Sachin C. Patwardhan, Auteur ; Ravi Methekar, Auteur Année de publication : 2010 Article en page(s) : pp. 148–165 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Development--Closed--Nonlinear--Predictive--Control--Class--Wiener  Models Résumé : Nonlinear model predictive control (NMPC) is increasingly being used for controlling microscale and system-on-chip devices, which exhibit complex and very fast dynamics. For effective control of such systems it is necessary to develop computationally efficient approaches for solving the NMPC problem. In this work, a Wiener type model has been used for capturing dynamics of multivariable nonlinear systems with fading memory. The resulting discrete nonlinear state space model is used to generate multistep predictions and formulate an unconstrained NMPC problem. A closed form solution to this problem is constructed analytically using the theory of solutions of quadratic operator polynomials. The effectiveness of the resulting quadratic control law is demonstrated by conducting simulation studies on a proton exchange membrane fuel cell (PEMFC) system, which exhibits fast dynamics and input multiplicity behavior. The quadratic control law is expected to control the PEMFC at its optimum (singular) operating point. The proposed laws achieve a fast and smooth transition from a suboptimal operating point to the optimum operating point with significantly small computation time. The proposed law is also found to be robust in the face of moderate perturbation in the unmeasured disturbances. The simulation results are validated by conducting experimental studies on a single cell PEMFC system and a benchmark heater−mixer setup that exhibits input multiplicity behavior. Through the experimental studies, we demonstrate that the proposed quadratic control law is able to operate the system at a singular operating point and establish the feasibility of employing the proposed control law for systems with very fast dynamics. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801284b [article] Development of a closed form nonlinear predictive control law based on a class of wiener models [texte imprimé] / Shraddha Deshpande, Auteur ; Sachin C. Patwardhan, Auteur ; Ravi Methekar, Auteur . - 2010 . - pp. 148–165. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 148–165 Mots-clés : Development--Closed--Nonlinear--Predictive--Control--Class--Wiener  Models Résumé : Nonlinear model predictive control (NMPC) is increasingly being used for controlling microscale and system-on-chip devices, which exhibit complex and very fast dynamics. For effective control of such systems it is necessary to develop computationally efficient approaches for solving the NMPC problem. In this work, a Wiener type model has been used for capturing dynamics of multivariable nonlinear systems with fading memory. The resulting discrete nonlinear state space model is used to generate multistep predictions and formulate an unconstrained NMPC problem. A closed form solution to this problem is constructed analytically using the theory of solutions of quadratic operator polynomials. The effectiveness of the resulting quadratic control law is demonstrated by conducting simulation studies on a proton exchange membrane fuel cell (PEMFC) system, which exhibits fast dynamics and input multiplicity behavior. The quadratic control law is expected to control the PEMFC at its optimum (singular) operating point. The proposed laws achieve a fast and smooth transition from a suboptimal operating point to the optimum operating point with significantly small computation time. The proposed law is also found to be robust in the face of moderate perturbation in the unmeasured disturbances. The simulation results are validated by conducting experimental studies on a single cell PEMFC system and a benchmark heater−mixer setup that exhibits input multiplicity behavior. Through the experimental studies, we demonstrate that the proposed quadratic control law is able to operate the system at a singular operating point and establish the feasibility of employing the proposed control law for systems with very fast dynamics. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801284b

Fast offset-free nonlinear model predictive control based on moving horizon estimation / Rui Huang in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7882–7890 Titre : Fast offset-free nonlinear model predictive control based on moving horizon estimation Type de document : texte imprimé Auteurs : Rui Huang, Auteur ; Lorenz T. Biegler, Auteur ; Sachin C. Patwardhan, Auteur Année de publication : 2010 Article en page(s) : pp 7882–7890 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Predictive  control  Nonlinear  model. Résumé : To deal with plant−model mismatches in control practice, this paper proposes two variations of an offset-free framework which integrates nonlinear model predictive control (NMPC) and moving horizon estimation (MHE). We prove that the proposed method achieves offset-free regulatory behavior, even in the presence of plant−model mismatches. If the plant uncertainty structure is known, the MHE can be tuned to estimate uncertainty parameters, to remove the plant−model mismatch online. In addition, we incorporate the advanced step NMPC (as-NMPC) and the advanced step MHE (as-MHE) strategies into the proposed method to reduce online computational delay. Finally, the proposed method is applied on a large scale air separation unit, and the steady state offset-free behavior is observed. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901945y [article] Fast offset-free nonlinear model predictive control based on moving horizon estimation [texte imprimé] / Rui Huang, Auteur ; Lorenz T. Biegler, Auteur ; Sachin C. Patwardhan, Auteur . - 2010 . - pp 7882–7890. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7882–7890 Mots-clés : Predictive  control  Nonlinear  model. Résumé : To deal with plant−model mismatches in control practice, this paper proposes two variations of an offset-free framework which integrates nonlinear model predictive control (NMPC) and moving horizon estimation (MHE). We prove that the proposed method achieves offset-free regulatory behavior, even in the presence of plant−model mismatches. If the plant uncertainty structure is known, the MHE can be tuned to estimate uncertainty parameters, to remove the plant−model mismatch online. In addition, we incorporate the advanced step NMPC (as-NMPC) and the advanced step MHE (as-MHE) strategies into the proposed method to reduce online computational delay. Finally, the proposed method is applied on a large scale air separation unit, and the steady state offset-free behavior is observed. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901945y

Midterm supply chain planning under uncertainty / Kishalay Mitra in Industrial & engineering chemistry research, Vol. 47 n°15 (Août 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°15 (Août 2008) . - p. 5501–5511 Titre : Midterm supply chain planning under uncertainty : a multiobjective chance constrained programming framework Type de document : texte imprimé Auteurs : Kishalay Mitra, Auteur ; Ravindra D. Gudi, Auteur ; Sachin C. Patwardhan, Auteur ; Gautam Sardar, Auteur Année de publication : 2008 Article en page(s) : p. 5501–5511 Note générale : Bibliogr. p. 5510-5511 Langues : Anglais (eng) Mots-clés : Chain  planning;  Chance  constrained  programming  approach Résumé : Uncertainty issues associated with a multisite, multiproduct supply chain planning problem has been analyzed in this paper, using the chance constrained programming (CCP) approach. In the literature, such problems have been addressed using the scenario-based two-stage stochastic programming approach. Although this approach has merits, in terms of decomposition, the computational complexity, even for small-size planning problems, is generally quite large, leading to either huge time consumption in solving the problem or an inability to solve big instances of problems under a standard solver environment. To make the aforementioned lacunea of two-stage stochastic programming more tractable, the problems under uncertainty have been recast in this paper in a CCP framework that uses a more suitable representation of uncertainty. Addressing uncertainty issues in product demands and machine uptime, using the CCP approach, leads to the evaluation of multiobjective tradeoffs that are analyzed here in the Pareto sense, and the ε-constraint approach is used to generate those Pareto optimal (PO) points. Different aspects of uncertainty issues are analyzed in detail by taking a few PO points among the total set of PO solutions found for this problem. It is seen that this CCP-based approach is quite generic, relatively simple to use and can be adapted for bigger size planning problems, as the equivalent deterministic problem does not blow up in size with the CCP approach. We demonstrate the analysis on a relatively moderate size midterm planning problem taken from published work [McDonald, C. M.; Karimi, I. A. Ind. Eng. Chem. Res. 1997, 36, 2691] and discuss various aspects of uncertainty in the context of this problem. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0710364 [article] Midterm supply chain planning under uncertainty : a multiobjective chance constrained programming framework [texte imprimé] / Kishalay Mitra, Auteur ; Ravindra D. Gudi, Auteur ; Sachin C. Patwardhan, Auteur ; Gautam Sardar, Auteur . - 2008 . - p. 5501–5511. Bibliogr. p. 5510-5511 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°15 (Août 2008) . - p. 5501–5511 Mots-clés : Chain  planning;  Chance  constrained  programming  approach Résumé : Uncertainty issues associated with a multisite, multiproduct supply chain planning problem has been analyzed in this paper, using the chance constrained programming (CCP) approach. In the literature, such problems have been addressed using the scenario-based two-stage stochastic programming approach. Although this approach has merits, in terms of decomposition, the computational complexity, even for small-size planning problems, is generally quite large, leading to either huge time consumption in solving the problem or an inability to solve big instances of problems under a standard solver environment. To make the aforementioned lacunea of two-stage stochastic programming more tractable, the problems under uncertainty have been recast in this paper in a CCP framework that uses a more suitable representation of uncertainty. Addressing uncertainty issues in product demands and machine uptime, using the CCP approach, leads to the evaluation of multiobjective tradeoffs that are analyzed here in the Pareto sense, and the ε-constraint approach is used to generate those Pareto optimal (PO) points. Different aspects of uncertainty issues are analyzed in detail by taking a few PO points among the total set of PO solutions found for this problem. It is seen that this CCP-based approach is quite generic, relatively simple to use and can be adapted for bigger size planning problems, as the equivalent deterministic problem does not blow up in size with the CCP approach. We demonstrate the analysis on a relatively moderate size midterm planning problem taken from published work [McDonald, C. M.; Karimi, I. A. Ind. Eng. Chem. Res. 1997, 36, 2691] and discuss various aspects of uncertainty in the context of this problem. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0710364

Online fault diagnosis in nonlinear systems using the multiple operating regime approach / Anjali P. Deshpande in Industrial & engineering chemistry research, Vol. 47 N°17 (Septembre 2008) 

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Online sensor/actuator failure isolation and reconfigurablecontrol using the generalized likelihood ratio method / Anjali P. Deshpande in Industrial & engineering chemistry research, Vol. 48 N°3 (Février 2009) 

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Resiliency issues in integration of scheduling and control / Kishalay Mitra in Industrial & engineering chemistry research, Vol. 49 N° 1 (Janvier 2010) 

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