Model Predictive Control of engine intake manifold pressure with an uncertain model⁎This work took place in the framework of the OpenLab "Electronics and Systems for Automotive" combining IMS laboratory and Stellantis company. Issue 6 (2021)

• Record Type: Journal Article
• Authors: Shulga, Evgeny; Lanusse, Patrick; Airimitoaie, Tudor-Bogdan; Maurel, Stéphane; Trutet, Arnaud
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 6(2021)
• Page Start: 335
• Page End: 340
• Publication Date: 2021
• Subjects: model predictive control -- extended Kalman filter -- combustion engine -- air path -- robustness; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.566
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper proposes a Model Predictive Control (MPC) design method for the intake manifold pressure of an internal combustion engine. This controller uses a nominal nonlinear model of the physical system to optimize a cost function. The noise sensibility of MPC is reduced and the robustness of the state estimation is achieved by using an Extended Kalman Filter (EKF). The proposed approach is evaluated using the Matlab Simulink model of the intake manifold of a spark ignition combustion engine in which parametric uncertainties and saturation are added. Different settings for EKF and MPC are tested for comparison purposes.