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Approximate Optimal Control of Discrete I/O Systems with C/GMRES

  • J. Blumenschein
  • , Thomas Schwarzgruber
  • , Roman Schmied
  • , Thomas Ernst Passenbrunner
  • , Harald Siegfried Waschl
  • , Luigi Del Re

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

Abstract

This paper proposes an optimal control approach to control systems described by discrete time I/O models. In particular, the C/GMRES method, which is a curse-of- dimensionality free and fast approximative optimal control method is adapted to this purpose. Comparing this approach with conventional industry standard methods, the benefits are twofold: First, easy and intuitive tuning of the control via the quadratic cost function of the optimization problem, reducing the necessary parameterization and tuning effort typical in industrial applications. Second, direct use of discrete time I/O models, typically the result of data based modeling efforts, which themselves can reduce development time drastically, compared to classical first principles and parameterization methods. To demonstrate the possible advantages of this method, it is applied to a problem, where tedious control parametrization and the resulting tuning efforts are state of the art: The control of the Diesel engine airpath is a MIMO system tracking problem, where the achieved tracking error of the outputs are decisive of the main emissions of the Diesel engine.
Original languageEnglish
Title of host publication2015 European Control Conference (ECC)
Pages104-110
Number of pages7
ISBN (Electronic)9783952426937
DOIs
Publication statusPublished - 16 Nov 2015

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Fields of science

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

JKU Focus areas

  • Mechatronics and Information Processing

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