Nonlinear system identification with applications to selective catalytic reduction systems

Sammanfattning: The stringent regulations on the emissions levels of heavy duty vehicles create a demandfor new methods of reducing harmful emissions from the engine. In order to be able tofollow these increasingly stricter legislations, complex aftertreatment systems are used.Achievement of optimal performance of these systems requires accurate models that canbe used for control design. As a result, the interest in modelling and control of aftertreatmentsystems has increased.This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavyduty vehicles using the selective catalyst as an aftertreatment system for its reduction.The process of the selective catalytic reduction (SCR) is nonlinear since the chemicalreactions involved are highly depending on the operating point. The momentary operatingpoint is defined by the driving profile of the vehicle which, for example, includes cold andhot engine starts, highway and urban driving.The purpose of this thesis is to investigate different methods for nonlinear system identificationof SCR systems with control in mind. The first two papers contain the theoreticalwork of this thesis. The first paper deals with improvement of an existing recursiveprediction error method (RPEM) where a more accurate discretisation algorithm wasused to improve the accuracy of the estimated nonlinear model. The second paper dealswith analysis of the convergence properties of the algorithm. For this analysis severalconditions were formulated that link the global and local convergence properties of thealgorithm to stability properties of an associated differential equation. Global convergenceto a stationary point was shown. In the third paper, the RPEM is used for identificationof the SCR system and finally the fourth paper a Hammerstein-Wiener model for identificationof the SCR system is applied. In both these cases the black-box models couldpredict the NOx behaviour of the SCR system quite well. The nonlinear models wereshown to describe the SCR system more accurately than linear models.