Automated nonlinear model predictive control using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Benyamin Grosman and Daniel R. Lewin",
  title =        "Automated nonlinear model predictive control using
                 genetic programming",
  journal =      "Computers \& Chemical Engineering",
  year =         "2002",
  volume =       "26",
  pages =        "631--640",
  number =       "4-5",
  owner =        "wlangdon",
  keywords =     "genetic algorithms, genetic programming, Empirical
                 process modeling, Nonlinear model predictive control",
  ISSN =         "0098-1354",
  URL =          "",
  DOI =          "doi:10.1016/S0098-1354(01)00780-3",
  abstract =     "This paper 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 is
                 expected to improve on the performance obtained using
                 linear models. The GP approach and the nonlinear MPC
                 strategy are described, and demonstrated by simulation
                 on two multivariable process: a mixing tank, which
                 involves only moderate nonlinearities, and the more
                 complex Karr liquid-liquid extraction column.",

Genetic Programming entries for Benyamin Grosman Daniel R Lewin