Control of Selective Catalytic Reduction Systems Using Feedback Linearisation

This paper discusses the identification and control of a selective catalytic reduction (SCR) system. SCR after‐treatment systems form an important technology for reducing the nitrogen oxides, NO_x, produced by diesel engines. To be able to control the system, i.e. reducing the output NO_x, good models of the after‐treatment system are essential. In this paper a nonlinear black‐box model is identified using a recursive prediction error method. The nonlinear model is applied for design of a controller using feedback linearization techniques including an adaptive strategy. A linear quadratic Gaussian controller is used for the control of the linearized system. A total of 17 parameters were estimated for the nonlinear model. The results indicate that output NO_x control using feedback linearization based on a second order black‐box nonlinear model is feasible, provided that identification or adaptivity is used for model tuning. The latter requirement is a result of a study of the robustness. In summary, the paper indicates that significant improvements as compared to linear control can be obtained with the proposed strategy.