MPC using nonlinear models generated by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Grosman2001663,
  author =       "Benjamin Grosman and Daniel R. Lewin",
  title =        "MPC using nonlinear models generated by genetic
                 programming",
  editor =       "Rafiqul Gani and Sten Bay Jorgensen",
  booktitle =    "European Symposium on Computer Aided Process
                 Engineering - 11, 34th European Symposium of the
                 Working Party on Computer Aided Process Engineering",
  publisher =    "Elsevier",
  year =         "2001",
  volume =       "9",
  pages =        "663--668",
  series =       "Computer Aided Chemical Engineering",
  address =      "Kolding, Denmark",
  month =        may # " 27-30",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "0-444-5070904",
  ISSN =         "1570-7946",
  DOI =          "doi:10.1016/S1570-7946(01)80105-X",
  URL =          "http://www.sciencedirect.com/science/article/B8G5G-4P40D5J-3R/2/96212e409c54e5c4c1781f7f1780816e",
  abstract =     "Publisher Summary

                 This chapter describes the use of genetic programming
                 (GP) to generate an empirical dynamic model of a
                 process and its use in a nonlinear model predictive
                 control (NMPC) strategy. GP derives both a model
                 structure and its parameter values in such a way that
                 the process trajectory is predicted accurately.
                 Consequently, the performance of the NMPC strategy,
                 based on this model, is expected to be good. The
                 genetic programming approach and the NMPC strategy are
                 briefly described and demonstrated by simulation on a
                 multivariable process. The application of GP-NMPC on
                 the control of a mixing tank is also discussed.
                 Discrete input-output models are generated to allow the
                 prediction of level and concentration trajectories
                 using the GP. Rapid acquisition of an empirical
                 nonlinear model is achieved efficiently using GP. This
                 model provides reliable prediction of future output
                 trajectories in the NMPC scheme, which also accounts
                 for both process interactions and constraint
                 violations, and thus, allows the computation of
                 improved control moves. Currently, work is in progress
                 on the application of the approach on a more complex
                 multiple-input, multiple-output (MIMO) process, a
                 simulation of a Karr liquid-liquid extraction column.",
  notes =        "ESCAPE-11",
}

Genetic Programming entries for Benyamin Grosman Daniel R Lewin

Citations