The purpose of this study is to enhance control strategies for selective catalytic reduction (SCR) and ammonia slip catalyst (ASC) systems, aiming to effectively reduce NOx emissions from automotive engines during realistic driving cycles. Despite the effectiveness of these after-treatment systems (ATS), their dynamic and non-linear characteristics present significant challenges in achieving precise control. Therefore, this research proposes a hybrid approach that combines backward induction (BI) as the primary optimization technique with model predictive control (MPC) framework for real-time application. The article introduces a reduced-state control-oriented model of the SCR + ASC system, which is embedded into the BI algorithm to calculate optimal control actions within a finite horizon. Additionally, it is proposed an alternative approach for adapting the grid of model states within the BI algorithm, effectively reducing the computational cost. This adjustment enables the algorithm to operate in real-time with near-optimal results, as confirmed by experimental validation. Lastly, the study explores how different degrees of knowledge regarding system disturbances impact the strategy’s performance, examining three distinct scenarios: constant prediction horizon, probabilistic description, and full knowledge of the prediction horizon.