Automatic Generation of Nonlinear Optimal Control Laws for Broom Balancing using Evolutionary Programming   [OC] [EP]

by

Chellapilla, K.

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Info: Proceedings of the 1998 IEEE World Congress on Computational Intelligence (Conference proceedings), 1998, p. 195-200
Keywords:genetic algorithms, genetic programming
Abstract:
This paper explores the use of mutation operators with evolutionary programming [EP] (EP) to automatically generate time optimal "bang-bang" type control laws for the three dimensional broom balancing (inverted pendulum) problem. EP produces a time optimal nonlinear control strategy that takes the state variables as input and determines the direction of the "bang-bang" force to be applied. Preliminary results indicate that the control laws generated are capable of generalizing over previously unseen input states and compare well with nonlinear control laws that were generated using other evolutionary computation methods. [EC] [ECM]
Notes:
ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence. Comparison with koza:book results
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BibTex:
@InProceedings{chellapilla:1998:agnoclbbEP,
  author =       "Kumar Chellapilla",
  title =        "Automatic Generation of Nonlinear Optimal Control Laws
                 for Broom Balancing using Evolutionary Programming",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "195--200",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c034.pdf",
  size =         "6 pages",
  abstract =     "This paper explores the use of mutation operators with
                 evolutionary programming (EP) to automatically generate
                 time optimal {"}bang-bang{"} type control laws for the
                 three dimensional broom balancing (inverted pendulum)
                 problem. EP produces a time optimal nonlinear control
                 strategy that takes the state variables as input and
                 determines the direction of the {"}bang-bang{"} force
                 to be applied. Preliminary results indicate that the
                 control laws generated are capable of generalizing over
                 previously unseen input states and compare well with
                 nonlinear control laws that were generated using other
                 evolutionary computation methods.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence.
                 Comparison with koza:book results",
}