Composite Adaptive Internal Model Control Theory and Applications to Engine Control

Abstract

To meet customer demands for vehicle performance and to satisfy increasingly stringent emissions standards, powertrain control strategies have become more complex. Thus, control system development and calibration present a costly and time-consuming challenge to the automotive industry. This thesis aims to develop new control methodologies that reduce the calibration effort. Internal Model Control (IMC) lends itself to automotive applications for its intuitive control structure with simple tuning philosophy. A plant model and a plant inverse are explicit components of IMC. We propose the Composite Adaptive IMC (CAIMC), which simultaneously identifies the model and the inverse, and reduces the tracking error through online identification. The constraint imposed by the stability of an n-th order plant is non-convex, and is re-parameterized as a linear matrix inequality. The identification problem with the stability constraint is reformulated as a convex programming problem. CAIMC is applied to the boost-pressure control problem of a turbocharged gasoline engine: first on a proprietary Simulink model of a turbocharged gasoline engine, then on the turbocharged 2 Liter four-cylinder gasoline engine in a Ford Explorer. Both simulation and experiment show that CAIMC is not only effective, but also drastically reduces the calibration effort compared to the traditional PI controller with feedforward.