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Multimodel Nonlinear Predictive Control with Gaussian Process Model

  • Ming Hu
  • Zonghai Sun
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 238)

Abstract

In industrial practice, parameters of plants are often not fixed in production process. The variation of parameters gives rise to the variation of system model. With model-based predictive control, the plants will be out of control if a fixed predictive model is applied when the parameters of plants change frequently. This paper proposed a multimodel nonlinear predictive control based on Gaussian process models which can be applied to the nonlinear system control with varying parameters. On the basis of the management ability of Gaussian process in fitting nonlinear model, a feasible switch strategy based on deviation of the predictive output from the actual output was applied to identify change of parameters. A two order dynamical system with four models switch was demonstrated to illustrate this algorithm. According to simulation results, this switch strategy can identify the change of system model accurately and quickly.

Keywords

Support Vector Machine Model Predictive Control Reference Trajectory Markov Chain Monte Carlo Method Switch Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Qin SJ, Badgwell TA (1997) An overview of industrial model predictive control technology. American Institute of Chemical Engineers, New YorkGoogle Scholar
  2. 2.
    Maciejowski JM (2000) Predictive control with constraints. Prentice Hall, LondonGoogle Scholar
  3. 3.
    Qin SJ, Badgwell TA (2000) Nonlinear model predictive control: an overview of nonlinear model predictive control applications. Prog Syst Control Theory 26:369–392Google Scholar
  4. 4.
    Camacho EF, Bordons C (2007) Nonlinear model predictive control: an introductory review. Assessment of Future Directions Nonlinear Model Predictive Control, Springer, Heidelberg 358:1–16MathSciNetCrossRefGoogle Scholar
  5. 5.
    Grüne L, Pannek J (2011) Nonlinear model predictive control: theory and algorithms. Springer Science+Business Media, BerlinCrossRefGoogle Scholar
  6. 6.
    Ažman K, Kocijan J (2011) Dynamical systems identification using Gaussian process models with incorporated local models. Eng Appl Artif Intel 24:398–408CrossRefGoogle Scholar
  7. 7.
    Kocijan J, Murray-Smith R (2005) Nonlinear predictive control with a Gaussian process model. Springer Science+Business Media, BerlinGoogle Scholar
  8. 8.
    Ažman K, Kocijan J (2008) Nonlinear model predictive control for model with local information and uncertainties. Inst Meas control 30:371–396CrossRefGoogle Scholar
  9. 9.
    Palma FD, Magni L (2004) A multi-model structure for model predictive control. Annu Rev Control 28:47–52CrossRefGoogle Scholar
  10. 10.
    Bao ZJ, Sun YX (2008) Support vector machine-based multi-model predictive control. J Control Theory Appl 6:305–310Google Scholar
  11. 11.
    Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT, CambridgeMATHGoogle Scholar
  12. 12.
    Matko D, Škrjanc I, Mušič G (2000) Robustness of fuzzy control and its application to a thermal plant. Math Comput Simul 51:245–255CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.College of Automation Science and EngineeringSouth China University of TechnologyGuangzhouChina

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