Title :
A softly switched multiple Model Predictive Control of a turbocharged Diesel engine
Author :
Junqiang Zhou ; Fiorentini, Lisa ; Canova, Marcello
Author_Institution :
Center for Automotive Res., Ohio State Univ., Columbus, OH, USA
Abstract :
The paper presents a Model Predictive Control (MPC) design for a Diesel engine air path system equipped with Variable Geometry Turbine (VGT), Exhaust Gas Recirculation (EGR) and a Variable Geometry Compressor (VGC). Starting from a validated nonlinear engine model, multiple linearized models were obtained at different operating conditions and a switched linear MPC controller was designed to deal with the nonlinearity of the system. However, such a hard switching controller can generate unexpected outcomes due to the abrupt changes when crossing the switching boundaries. In order to mitigate the issues related to switching-type controllers, a softly switched mechanism is proposed here, which uses a convex combination of the objective functions during the switching process. A suboptimal solution is then adopted to reduce the computational complexity, allowing one to derive the explicit solution of the finite horizon optimization problem. Finally, simulation studies are conducted, and the results confirm the effectiveness of the proposed methodologies.
Keywords :
compressors; computational complexity; control system synthesis; convex programming; diesel engines; exhaust systems; fuel systems; linear systems; predictive control; time-varying systems; turbines; MPC design; computational complexity; convex objective function combination; diesel engine air path system; exhaust gas recirculation; finite horizon optimization problem; hard switching controller; linearized models; nonlinear engine model; softly switched multiple model predictive control; switched linear MPC controller; turbocharged diesel engine; variable geometry compressor; variable geometry turbine; Atmospheric modeling; Engines; Mathematical model; Optimization; Surges; Switches; Automotive; Optimal control; Predictive control for nonlinear systems;
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
Print_ISBN :
978-1-4799-3272-6
DOI :
10.1109/ACC.2014.6859356