Composite Adaptive Internal Model Control and Its Application to Boost Pressure Control of a Turbocharged Gasoline Engine

Abstract

Internal model control (IMC) explicitly incorporates the plant model and its approximate inverse and offers an intuitive controller structure and calibration procedure. In the presence of plant-model uncertainty, combining the IMC structure with parameter estimation through the certainty equivalence principle leads to adaptive IMC (AIMC), where either the plant model or its inverse is identified. This paper proposes a composite AIMC (CAIMC) that explores the IMC structure and simultaneous plant dynamics and inverse dynamics identification to achieve improved performance of AIMC. A toy plant is used to illustrate the feasibility and potential of CAIMC. The advantages of CAIMC are later demonstrated on the boost-pressure control problem of a turbocharged gasoline engine. The design of the CAIMC assumes that the plant model and its inverse are represented by the first-order linear dynamics. The unmodeled dynamics and uncertainties due to linearization and variations in operating conditions are compensated through adaptation. The resulting CAIMC is first applied to a physics-based high-order and nonlinear proprietary turbocharged gasoline engine model, and then validated on a turbocharged 2-L four-cylinder gasoline engine on a vehicle with vacuum-actuated wastegate. Both the simulation and experimental results show that the CAIMC cannot only effectively compensate for uncertainties but also auto-tune the IMC controller for the best performance.