Optimisation of Time Domain Controllers for Supply Ships Using Genetic Algorithms and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Maria Eva {Alfaro Cid}",
  title =        "Optimisation of Time Domain Controllers for Supply
                 Ships Using Genetic Algorithms and Genetic
  school =       "The University of Glasgow",
  year =         "2003",
  address =      "Glasgow, UK",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://casnew.iti.es/papers/ThesisEva.pdf",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=49&uin=uk.bl.ethos.398769",
  size =         "348 pages",
  abstract =     "The use of genetic methods for the optimisation of
                 propulsion and heading controllers for marine vessels
                 is presented in this thesis. The first part of this
                 work is a study of the optimisation, using Genetic
                 Algorithms, of controller designs based on a number of
                 different time-domain control methodologies such as
                 PID, Sliding Mode, H? and Pole Placement. These control
                 methodologies are used to provide the structure for
                 propulsion and navigation controllers for a ship. Given
                 the variety in the number of parameters to optimise and
                 the controller structures, the Genetic Algorithm is
                 tested in different control optimisation problems with
                 different search spaces. This study presents how the
                 Genetic Algorithm solves this minimisation problem by
                 evolving controller parameters solutions that
                 satisfactorily perform control duties while keeping
                 actuator usage to a minimum. A variety of genetic
                 operators are introduced and a comparison study is
                 conducted to find the Genetic Algorithm scheme best
                 suited to the parameter controller optimisation
                 problem. The performance of the four control
                 methodologies is also compared. A variation of Genetic
                 Algorithms, the Structured Genetic Algorithm, is also
                 used for the optimisation of the H? controller. The H?
                 controller optimisation presents the difficulty that
                 the optimisation focus is not on parameters but on
                 transfer functions. Structured Genetic Algorithm
                 incorporates hierarchy in the representation of
                 solutions making it very suitable for structural
                 optimisation. The H? optimisation problem has been
                 found to be very appropriate for comparing the
                 performance of Genetic Algorithms versus Structured
                 Genetic Algorithm. During the second part of this work,
                 the use of Genetic Programming to optimise the
                 controller structure is assessed. Genetic Programming
                 is used to evolve control strategies that, given as
                 inputs the current and desired state of the propulsion
                 and heading dynamics, generate the commanded forces
                 required to manoeuvre the ship. Two Genetic Programming
                 algorithms are implemented. The only difference between
                 them is how they generate the numerical constants
                 needed for the solution of the problem. The first
                 approach uses a random generation of constants while
                 the second approach uses a combination of Genetic
                 Programming with Genetic Algorithms. Finally, the
                 controllers optimised using genetic methods are
                 evaluated through computer simulations and real
                 manoeuvrability tests in a laboratory water basin
                 facility. The robustness of each controller is analysed
                 through the simulation of environmental disturbances.
                 Also, optimisations in presence of disturbances are
                 carried out so that the different controllers obtained
                 can be compared. The particular vessels used in this
                 study are two scale models of a supply ship called
                 CyberShip I and CyberShip II. The results obtained
                 illustrate the benefits of using Genetic Algorithms and
                 Genetic Programming to optimise propulsion and
                 navigation controllers for surface ships.",
  notes =        "uk.bl.ethos.398769",

Genetic Programming entries for Eva Alfaro-Cid