Control of Biomass Grate Boilers using Internal Model Control

Christoph Schörghuber, Markus Gölles*, Markus Reichhartinger, Martin Horn

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

A new model-based control strategy for biomass grate boilers is presented in this paper. Internal model control is used to control four outputs of the plant and to achieve a control structure with fewer control parameters needing to be experimentally tuned. A nonlinear state–space model describing the essential behaviour of the biomass grate boiler is used for controller design. The inverse system dynamics representing the main part of internal model control are designed with the help of this model. In doing so the properties of differentially flat systems are used. Due to a time delayed input, the inverse system is determined only for three input output channels. The stabilization of the inverse system dynamics, however, is a challenging task. A stabilization method with the help of the time delayed input is suggested and a stability analysis is given. The new control strategy has only three parameters to be tuned, representing a major reduction of complexity in comparison to existing model-based approaches. Finally, experimental results of the implemented control strategy on representative biomass grate boiler with a nominal capacity of 180 kW are presented and compared to an existing model-based control strategy based on input output linearization. The experimental evaluation proves that it is possible to operate the biomass boiler in all load ranges with high efficiency and low pollutant emissions.
Original languageEnglish
Article number104274
Number of pages10
JournalControl Engineering Practice
Volume96
DOIs
Publication statusPublished - Mar 2020

Keywords

  • Biomass
  • Combustion
  • Experimental results
  • Flatness based control
  • Internal model control
  • Model-based control
  • Nyquist stability criterion

ASJC Scopus subject areas

  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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