Automated synthesis of a local model network based nonlinear model predictive controller applied to the engine air path. (May 2021)

• Record Type: Journal Article
• Authors: Euler-Rolle, Nikolaus; Krainer, Ferdinand; Jakubek, Stefan; Hametner, Christoph
• Is Part Of: Control engineering practice. Volume 110(2021)
• Publication Date: 2021-05
• Subjects: Nonlinear control -- Model predictive control -- Differential flatness -- Multivariate engine control; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104768
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: The efficient and emission reducing control of the air path of an internal combustion engine is a challenging task due to its nonlinear and multivariate nature. By applying the well-known local model network approach to describe the nonlinear process in terms of linear operating point approximations, a fast and efficient model generation through data-driven system identification can be achieved. In this paper it is demonstrated how a nonlinear multivariate model predictive controller can be synthesised from the identified model directly by exploiting its representation in flatness coordinates. For the proposed controller, a compactly formulated quadratic programme results. Because of the uniform representation of all local models in controllability canonical form, a state observer is rendered unnecessary. Additionally, input and output constraints can be taken into account in the optimisation directly. The effectiveness of the control scheme is demonstrated successfully in jointly controlling the exhaust manifold pressure and the engine out NO x concentration for a heavy-duty engine on the testbed. Highlights: A multivariate control concept for a heavy-duty diesel engine is presented. NOx concentration and exhaust manifold pressure are modelled by a local model network. The nonlinear model predictive controller is synthesised from the identified model. Coupling as well as input and output constraints are considered in control. Results on a heavy-duty engine testbed demonstrate the effectiveness of control.