Model predictive engine air-ratio control using online sequential extreme learning machine

Neural Computing and Applications

Volumn 27, Issue 1, 2016, Pages 79-92

  Wong, Pak Kin ^a   Wong, Hang Cheong ^a   Vong, Chi Man ^a   Xie, Zhengchao ^a   Huang, Shaojia ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Air ratio; Automotive engine; Nonlinear model predictive control; Online sequential extreme learning machine

Indexed keywords

CONTROLLERS; E-LEARNING; ENGINES; FUEL CONSUMPTION; KNOWLEDGE ACQUISITION; MODEL PREDICTIVE CONTROL; PREDICTIVE CONTROL SYSTEMS; PROPORTIONAL CONTROL SYSTEMS; RECURRENT NEURAL NETWORKS; THREE TERM CONTROL SYSTEMS;

AIR RATIO; AUTOMOTIVE ENGINE; BRAKE SPECIFIC FUEL CONSUMPTION; DIAGONAL RECURRENT NEURAL NETWORKS; EXTERNAL DISTURBANCES; MODEL PREDICTIVE CONTROLLERS; NONLINEAR MODEL PREDICTIVE CONTROL; ONLINE SEQUENTIAL EXTREME LEARNING MACHINE;

LEARNING SYSTEMS;

EID: 84953349048     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-014-1555-7     Document Type: Article

References (29)

1. Crouse WH, Anglin DL (1993) Automotive mechanics, 10th edn. McGraw-Hill, New York
2. Heinrich J, Schwarze PE, Stilianakis N, Momas I, Medina S, Totlandsdal AI, Bree LV, Kuna-Dibbert B, Krzyzanowski M (2005) Studies on health effects of transport-related air pollution. In: Krzyzanowski M, Kuna-Dibbert B, Schneider J (eds) health effects of transport-related air pollution. WHO Press, Geneva, pp 125–183
3. Campbell M, Bassil K, Morgan C, Lalani M, Macfarlane R, Bienefeld M (2007) Air pollution burden of illness from traffic in Toronto: problems and solutions. Toronto Public Health, Toronto
4. Millman A, Tang D, Perera FP (2008) Air pollution threatens the health of children in China. Pediatrics 122:620–628
5. Ji S, Cherry CR, Bechle MJ, Wu Y, Marshall JD (2012) Electric vehicles in China: emissions and health impacts. Environ Sci Technol 46:2018–2024
6. Yim SH, Barrett SR (2012) Public health impacts of combustion emissions in the United Kingdom. Environ Sci Technol 46:4291–4296
7. Gilles T (2011) Automotive service: inspection, maintenance, repair, 4th edn. Cengage Learning, Stamford
8. Żmudka Z, Postrzednik S (2011) Inverse aspects of the three-way catalytic converter operation in the spark ignition engine. J KONES Powertrain Transp 18:509–516
9. Wang SW, Yu DL, Gomm JB, Page GF, Douglas SS (2006) Adaptive neural network model based predictive control for air–fuel ratio of SI engines. Eng Appl Artif Intell 19:189–200
10. Zhai YJ, Yu DL (2008) Radial-basis-function-based feedforward-feedback control for air–fuel ratio of spark ignition engines. Proc Inst Mech Eng D J Automob Eng 222:415–428
11. Zhai YJ, Yu DL (2009) Neural network model-based automotive engine air/fuel ratio control and robustness evaluation. Eng Appl Artif Intell 22:171–180
12. Zhai YJ, Yu DW, Guo HY, Yu DL (2010) Robust air/fuel ratio control with adaptive DRNN model and AD tuning. Eng Appl Artif Intell 23:283–289
13. Li GY (2007) Application on intelligent control and MATLAB to electronically controlled engines, 1st edn. Publishing House of Electronics Industry, Beijing (in Chinese)
14. Wong PK, Tam LM, Li K, Vong CM (2010) Engine idle-speed system modelling and control optimization using artificial intelligence. Proc Inst Mech Eng D J Automob Eng 224:55–72
15. Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423
16. Wong KI, Wong PK, Cheung CS, Vong CM (2013) Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set. Appl Soft Comput 13:4428–4441
17. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of International Joint Conference on Neural Networks (IJCNN2004), Budapest, pp 985–990
18. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
19. Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybernet 2:107–122
20. Huang GB, Zhou HM, Ding XJ, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42:513–529
21. Chacko B, Krishnan VRV, Raju G, Anto PB (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybernet 3:149–161
22. Bueno-Crespo A, García-Laencina PJ, Sancho-Gómez J-L (2013) Neural architecture design based on extreme learning machine. Neural Netw 48:19–24
23. Wang X, Shao Q, Miao Q, Zhai J (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9
24. Miche Y, van Heeswijk M, Bas P, Simula O, Lendasse A (2011) TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization. Neurocomputing 74:2413–2421
25. Zhai J, Xu H, Wang X (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16:1493–1502
26. Zhai J, Xu H, Li Y (2013) Fusion of extreme learning machine with fuzzy integral. Int J Uncertain Fuzziness Knowl Based Syst 21(Suppl. 2):23–34
27. Luo J, Vong CM, Wong PK (2013) Sparse Bayesian extreme learning machine for multi-classification. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2013.2281839
28. Golub GH, Loan CFV (1996) Matrix computation, 3rd edn. Johns Hopkins University Press, Baltimore
29. Chandrupatla TR (1998) An efficient quadratic fit—sectioning algorithm for minimization without derivatives. Comput Methods Appl Mech Eng 152:211–217