Automated synthesis of optimal controller using multi-objective genetic programming for two-mass-spring system

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Gholaminezhad:2014:ICRoM,
  author =       "Iman Gholaminezhad and Ali Jamali and Hirad Assimi",
  booktitle =    "Second RSI/ISM International Conference on Robotics
                 and Mechatronics (ICRoM 2014)",
  title =        "Automated synthesis of optimal controller using
                 multi-objective genetic programming for two-mass-spring
                 system",
  year =         "2014",
  month =        oct,
  pages =        "041--046",
  abstract =     "There are much research effort in the literature using
                 genetic programming as an efficient tool for design of
                 controllers for industrial systems. In this paper,
                 multi-objective uniform-diversity genetic programming
                 (MUGP) is used for automated synthesis of both
                 structure and parameter tuning of optimal controllers
                 as a many-objective optimisation problem. In the
                 proposed evolutionary design methodology, each
                 candidate controller illustrated by a transfer
                 function, whose optimal structure and parameters,
                 obtained based on performance optimisation of each
                 candidate controller. The performance indices of each
                 controller are treated as separate objective functions,
                 and thus solved using the multi-objective method of
                 this work. A two-mass-spring system is considered to
                 show the efficiency of the proposed method using
                 performance optimisation of open loop and closed loop
                 control system characteristics. The results show that
                 the proposed method is a computationally efficient
                 framework compared to other methods in the literature
                 for automatically designing both structure and
                 parameter tuning of optimal controllers.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICRoM.2014.6990874",
  notes =        "Dept. of Mech. Eng., Univ. of Guilan, Rasht, Iran

                 Also known as \cite{6990874}",
}

Genetic Programming entries for Iman Gholaminezhad Ali Jamali Hirad Assimi

Citations