Title :
Data-driven model predictive control of Air-fuel Ratio for PFISI engine
Author :
Yunfeng Hu ; Yanan Fan ; Yu Liang ; Hong Chen
Author_Institution :
Dept. of Control Sci. & Eng., Jilin Univ., Changchun, China
Abstract :
Air-fuel Ratio (AFR) control is considered as one of the most important issues in engine control. In this paper, a data-driven model predictive controller is designed for AFR control of Port Fuel Injection Spark Ignition (PFISI) gasoline engine system. According to the input-output data of a engine simulation model provided by the commercial software enDYNA, the future dynamic of engine system can be predicted. Furthermore, based on the model predictive control (MPC) approach, the control requirement is converted to the optimal control objective, then the control action is obtained by solving the optimal problem. Finally, the simulation results show the effectiveness of the proposed controller.
Keywords :
control system synthesis; fuel systems; internal combustion engines; optimal control; predictive control; AFR control; MPC approach; PFISI engine; PFISI gasoline engine system; air-fuel ratio control; data-driven model predictive controller design; enDYNA; engine control; engine simulation model; engine system dynamic prediction; input-output data; optimal control objective; port fuel injection spark ignition gasoline engine system; Atmospheric modeling; Data models; Engines; Fuels; Mathematical model; Predictive control; Predictive models; AFR control; Data-driven Model Predictive Control; Engine;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053485
