G. Alcan, M. Unel, V. Aran, M. Yilmaz, C. Gurel, K. Koprubasi
18th IFAC Symposium on System Identification (SYSID 2018) Stockholm, Sweden, July 9-11, 2018
IFAC-PapersOnLine, Volume 51, Issue 15, Pages 168-173
Publication date: October, 2018


In this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with all input regressors, and the most significant input regressors are selected for the nonlinear prediction by orthogonal least square (OLS) algorithm and error reduction ratio. Experimental results show that NOx emissions can be modeled with high validation performance and models obtained using a reduced set of regressors perform better in terms of stability and robustness.


  • Diesel Engine
  • NOx Emission
  • Orthogonal Least Square
  • Regressor Selection
  • Sigmoid NARX


  title={Diesel engine NOx emission modeling using a new experiment design and reduced set of regressors},
  author={Alcan, Gokhan and Unel, Mustafa and Aran, Volkan and Yilmaz, Metin and Gurel, Cetin and Koprubasi, Kerem},