Nonlinear economic Model Predictive Control for SI engines based on Sequential Quadratic Programming

This paper proposes a model predictive torque control strategy for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR). The proposed Nonlinear (economic) Model Predictive Controller (NMPC) tries to minimize fuel consumption with given Indicate Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion stability. A Nonlinear Programming (NLP) problem is formulated and solved using Sequential Quadratic Programming (SQP) to obtain the desired control actions. The SQP exploits the Gauss-Newton like structure of the real time NLP problem to simplify computation of Hessian matrix. Simulation results demonstrate that the proposed model predictive IMEP control can track the IMEP reference for engine cycles without active constraints (with a RMS tracking error of 1.1%). When the IMEP reference conflicts with constraints, the SQP MPC can efficiently find close to optimal control actions that are similar to those from off-line feed forward calibration.