A Nonlinear Model Predictive Control Strategy with a Disturbance Observer for Spark Ignition Engines with External EGR

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset. The complete control system demonstrates strong disturbance rejection and closed loop stability. RMS error of IMEP reference tracking is 1.1% for engine cycles without active combustion constraints. Mean computation time for the proposed control system is 1.07 ms in the rapid-prototype engine controller, suggesting it is feasible for future ECUs. The proposed model predictive engine control strategy actively compensates for air path delay, resulting in higher EGR percentage and reduced combustion phasing retard as compared to conventional engine control strategies. FTP drive cycle simulation results of both conventional map-based engine control and the proposed E-NMPC on the same engine model suggest approximately 6.5% of fuel economy improvement.