PROJECT TITLE :

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

ABSTRACT:

Internal model management (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 understanding equivalence principle ends up in 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 realize improved performance of AIMC. A toy plant is employed to illustrate the feasibility and potential of CAIMC. The blessings of CAIMC are later demonstrated on the boost-pressure control problem of a turbocharged gasoline engine. The look of the CAIMC assumes that the plant model and its inverse are represented by the primary-order linear dynamics. The unmodeled dynamics and uncertainties because of linearization and variations in operating conditions are compensated through adaptation. The ensuing CAIMC is initial 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 catch up on uncertainties however conjointly auto-tune the IMC controller for the best performance.