Quantification of Linear Approximation Error for Model Predictive Control of Spark Ignited Turbocharged Engines
Modern turbocharged spark-ignition engines are being equipped with an increasing number of control actuators to simultaneously meet fuel economy, emissions and performance targets. The response time variations between a given set of engine control actuators tends to be significant during transients and necessitate highly complex actuator scheduling routines. Model Predictive Control (MPC) algorithms have the potential to significantly reduce calibration and control tuning efforts as compared to current methodologies that are designed around integration of multiple single-input single-output sub-system controllers. MPC systems simultaneously generate all actuator responses by using a combination of current engine conditions and optimization of a control-oriented plant model. To achieve real-time control the engine model and optimization processes must be computationally efficient without sacrificing effectiveness.