GP-based Motion Control Design for the Double-integrator System Subject to Velocity Constraint

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Ollin Penaloza-Mejia and Eddie Clemente and 
                 Marlen Meza-Sanchez and Cynthia B. Perez and 
                 Francisco Chavez",
  title =        "{GP-based} Motion Control Design for the
                 Double-integrator System Subject to Velocity
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "73--74",
  size =         "2 pages",
  URL =          "",
  DOI =          "doi:10.1145/3067695.3076094",
  acmid =        "3076094",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, bounded
                 velocity, double-integrator system, motion control
  month =        "15-19 " # jul,
  abstract =     "The motion control problem for the double-integrator
                 system subject to velocity constraint is addressed. A
                 novel methodology, which consists of a two-stage
                 process and regards a trade-off between natural and
                 learned behaviours to develop a family of analytic
                 controllers, is proposed. To this end, firstly, a
                 natural behaviour is designed to achieve asymptotic
                 tracking of a desired continuous trajectory by using a
                 Control-Theory approach. Secondly, learned behaviours
                 are discovered by using a Genetic Programming approach
                 to synthesize an analytic controller to ensure a
                 bounded velocity of the system. The integration of
                 these approaches allows the system to exhibit a good
                 tracking performance while keeping the velocity bounded
                 to a desired value, freely set by the user. Simulation
                 results are provided to illustrate the effectiveness of
                 the proposal, and a comparison with a traditional
                 Control-Theory-Based solution is also given and
  notes =        "Also known as
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Ollin Penaloza-Mejia Eddie Helbert Clemente Torres Marlen Meza-Sanchez Cynthia B Perez Francisco Chavez