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

Paper #:
  • 2017-01-0608

Published:
  • 2017-03-28
Abstract:
This paper proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and 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 Indicate 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 actions. 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 sensor/actuator offset. The complete control system demonstrated strong disturbance rejection and close 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 on the prototype engine controller, proving its feasibility 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 comparing to conventional engine control strategy. It shows approximately 7 percent of fuel economy improvement for the test vehicle over FTP driving cycle.
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