Explicit stochastic predictive control of combustion plants based on Gaussian process models

  • Authors:

  • Alexandra Grancharova;Juš Kocijan;Tor A. Johansen

  • Affiliations:

  • Institute of Control and System Research, Bulgarian Academy of Sciences, Acad. G. Bonchev street, Bl.2, P.O.Box 79, Sofia 1113, Bulgaria;Department of Systems and Control, Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia and Centre for Systems and Information Technologies, University of Nova Gorica, Vipavska 13, 5000 Nov ...;Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491 Trondheim, Norway

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2008

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Abstract

Energy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gases emissions and to increase the efficiency is to improve the control strategies of the existing thermoelectric power plants. This makes the Nonlinear Model Predictive Control (NMPC) method very suitable for achieving an efficient combustion control. Recently, an explicit approximate approach for stochastic NMPC based on a Gaussian process model was proposed. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation, which is an essential issue in safety-critical applications. This paper considers the application of an explicit approximate approach for stochastic NMPC to the design of an explicit reference tracking NMPC controller for a combustion plant based on its Gaussian process model. The controller brings the air factor (respectively the concentration of oxygen in the flue gases) on its optimal value with every change of the load factor and thus an optimal operation of the combustion plant is achieved.