Adaptive Model Predictive Control of an SCR Catalytic Converter System for Automotive Applications

摘要

Selective catalytic reduction (SCR) is coming into worldwide use for diesel engine emissions reduction of on- and off-highway vehicles. These applications are characterized by broad operating range as well as rapid and unpredictable changes in operating condition. Significant nonlinearity, input, and output constraints, and stringent performance requirements have led to the proposal of many different advanced control strategies. This article introduces a model predictive feedback controller based on a nonlinear, reduced order model. Computational effort is significantly reduced through successive linearization, analytical solutions, and a varying terminal cost function. A gradient-based parameter adaptation law is employed to achieve consistent performance. The controller is demonstrated in simulation for an on-highway heavy-duty diesel engine over two widely different emissions test cycles and for 24 different plants. Comparisons with baseline control designs reveal the attractive features as well as the limitations of this approach.