A Design Framework for Nonlinear Predictive Engine Control

Xiaoming Wang; Peter Ortner; Daniel Alberer

A Design Framework for Nonlinear Predictive Engine Control

Xiaoming Wang, Peter Ortner, Daniel Alberer

Institute of Design and Control of Mechatronical Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of the ECOSM09
Number of pages	7
Publication status	Published - Dec 2009

Fields of science

202 Electrical Engineering, Electronics, Information Engineering
202027 Mechatronics
202034 Control engineering
203027 Internal combustion engines
206001 Biomedical engineering
206002 Electro-medical engineering
207109 Pollutant emission

Cite this

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title = "A Design Framework for Nonlinear Predictive Engine Control",

abstract = "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.",

author = "Xiaoming Wang and Peter Ortner and Daniel Alberer",

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AU - Alberer, Daniel

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N2 - 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.

AB - 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.

M3 - Conference proceedings

BT - Proceedings of the ECOSM09

ER -