Data-driven Model Predictive Control for Lean NOx Trap Regeneration
作者： Milad Karimshoushtari , Carlo Novara , Antonino Trotta
DOI: 10.1016/J.IFACOL.2017.08.1436
关键词: Chemical process 、 Trap (computing) 、 Diesel fuel 、 Context (language use) 、 Data-driven 、 Co-simulation 、 NOx 、 Control theory 、 Computer science 、 Control theory 、 Model predictive control
摘要: Lean NOx Trap (LNT) is one of the most eective after-treatment technologies used to reduce emissions diesel engines. One relevant problem in this context LNT regeneration timing control. This indeed difficult due fact that LNTs are highly nonlinear systems, involving complex physical/chemical processes hard model. In paper, a novel data-driven model predictive control (D2-MPC) approach for proposed, allowing us overcome these issues. does not require physical engine/trap system but based on low-complexity polynomial prediction model, directly identied from data. The computed through an optimization algorithm, which uses predict behavior. proposed D2- MPC tested co-simulation study, where plant represented by detailed developed using well-known commercial tool AMEsim, and controller implemented Matlab/Simulink.
sci-hub.se 参考文章(9)
Simone Formentin, Carlo Novara, Sergio M. Savaresi, Mario Milanese, Active Braking Control System Design: The D $^{\bf 2}$ -IBC Approach IEEE-ASME Transactions on Mechatronics. ,vol. 20, pp. 1573- 1584 ,(2015) , 10.1109/TMECH.2015.2412172
Jonas Sjöberg, Qinghua Zhang, Lennart Ljung, Albert Benveniste, Bernard Delyon, Pierre-Yves Glorennec, Håkan Hjalmarsson, Anatoli Juditsky, Nonlinear black-box modeling in system identification: a unified overview Automatica. ,vol. 31, pp. 1691- 1724 ,(1995) , 10.1016/0005-1098(95)00120-8
Ming-Feng Hsieh, Junmin Wang, Marcello Canova, Two-Level Nonlinear Model Predictive Control for Lean NOx Trap Regenerations Journal of Dynamic Systems Measurement and Control-transactions of The Asme. ,vol. 132, pp. 041001- ,(2010) , 10.1115/1.4001710
J.Carlos Zavala, Pannag R. Sanketi, M. Wilcutts, T. Kaga, J.K. Hedrick, SIMPLIFIED MODELS OF ENGINE HC EMISSIONS, EXHAUST TEMPERATURE AND CATALYST TEMPERATURE FOR AUTOMOTIVE COLDSTART IFAC Proceedings Volumes. ,vol. 40, pp. 199- 205 ,(2007) , 10.3182/20070820-3-US-2918.00028
Kenneth Hsu, Carlo Novara, Tyrone Vincent, Mario Milanese, Kameshwar Poolla, Parametric and nonparametric curve fitting Automatica. ,vol. 42, pp. 1869- 1873 ,(2006) , 10.1016/J.AUTOMATICA.2006.05.024
Robert Tibshirani, Regression Shrinkage and Selection Via the Lasso Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 58, pp. 267- 288 ,(1996) , 10.1111/J.2517-6161.1996.TB02080.X
C. Novara, Sparse Identification of Nonlinear Functions and Parametric Set Membership Optimality Analysis IEEE Transactions on Automatic Control. ,vol. 57, pp. 3236- 3241 ,(2012) , 10.1109/TAC.2012.2202051
Carlo Novara, Simone Formentin, Sergio M. Savaresi, Mario Milanese, Data-driven design of two degree-of-freedom nonlinear controllers: The D2-IBC approach Automatica. ,vol. 72, pp. 19- 27 ,(2016) , 10.1016/J.AUTOMATICA.2016.05.010
C. Novara, T. Vincent, K. Hsu, M. Milanese, K. Poolla, Parametric identification of structured nonlinear systems Automatica. ,vol. 47, pp. 711- 721 ,(2011) , 10.1016/J.AUTOMATICA.2011.01.063