A Design Framework for Nonlinear Predictive Engine Control

Model predictive control (MPC) has been proposed several times for automotive control, with promising results, mostly based on a linear MPC approach. However, as most automotive systems are nonlinear, nonlinear MPC (NMPC) would be an interesting option. Unfortunately, an optimal control design with a generic nonlinear model usually leads to a complex, non convex problem. Against this background, this paper proposes a control system design based on a nonlinear system identification using a quasi linear parameter varying (LPV) structure, which is then used in a NMPC design framework. This paper presents the approach and the application to a well studied system, the air path of a Diesel engine.