Airpath prediction model and predictive control of turbocharged gasoline engine
CHEN Huan
HU Yun-feng
YU Shu-you
SUN Peng-yuan
CHEN Hong
Abstract：In this paper, a neural network based model predictive controller is developed for the coordinated control of the throttle and wastegate in a turbocharged gasoline engine airpath system. Firstly, considering the mixed description of map and physical for engine airpath system, a data-driven nonlinear airpath model is trained using back propagation neural network (BPNN) to predict the future dynamics of the turbocharged engine. Secondly, the prediction model is linearized based on Taylor expansion and the feasibility of this simplification is assessed. Thirdly, in order to satisfy the engine torque demand, a linear model predictive controller is designed to manage the throttle and wastegate so that the engine tracks the setpoints of the intake manifold pressure and boost pressure considering the system constraints. Furthermore, simulation results are presented to verify the effectiveness of the controller. Finally, a rapid control prototyping (RCP) experiment based on dSPACE is further implemented to test the real-time performance.
Keywords：turbocharged gasoline engine controlmodel predictive controlprediction modelBP neural network
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 1008-1018 )