Evolutionary Based Controller Design

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  title =        "Evolutionary Based Controller Design",
  author =       "Ivan Sekaj",
  booktitle =    "Evolutionary Computation",
  publisher =    "InTech",
  year =         "2009",
  editor =       "Wellington Pinheiro dos Santos",
  chapter =      "13",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-953-307-008-7",
  URL =          "http://www.intechopen.com/download/pdf/pdfs_id/10935",
  DOI =          "DOI:10.5772/9620",
  bibsource =    "OAI-PMH server at www.intechopen.com",
  language =     "eng",
  oai =          "oai:intechopen.com:10935",
  URL =          "http://www.intechopen.com/articles/show/title/evolutionary-based-controller-design",
  abstract =     "In this chapter evolutionary based design/optimisation
                 approaches has been proposed for controller design of
                 continuous-time process control. Parameters of
                 controllers with fixed defined internal structure are
                 designed as well as controllers with a-priori unknown
                 internal structure and its parameters. The presented
                 approaches minimise a cost function, which comprises
                 closed-loop system simulation and performance index
                 evaluation. In this way the controller design is
                 transformed into a search problem in the n-dimensional
                 parameter space. The design/optimisation can be carried
                 out for complex systems and control structures of
                 various types. The main and practically the only
                 limitation of the approach is the time consuming
                 computation (compared with conventional approaches) due
                 to thousands up to ten thousands closed-loop
                 simulations needed by each design procedure. From the
                 point of view the user, on the other hand, the design
                 method is simple to use. It transfers the design effort
                 from the experienced human designer to the computer.
                 The design approach is simple to extend to robust
                 controller design, for which two different methods have
                 been proposed. In addition statistical robustness
                 measure has been introduced, which can be considered as
                 an objective tool for robust controller performance
                 comparison. Next, the design idea has been extended
                 also to a multi-objective design task, where the
                 objective is the search for the Pareto-optimal set of
                 solutions. From these solutions the designer can choose
                 the representative, which is the most appropriate in
                 the particular case. Finally the design goal was
                 extended from a fix defined controller structure with
                 unknown controller parameters to the
                 search/optimisation of the unknown internal structure
                 of the controller. From that reason, the Genetic
                 Programming has been used. The proposed
                 evolutionary-based methods can be used for design of
                 various types of controllers for various system types
                 (linear, non-linear, stable, unstable, SISO and MIMO,
                 fuzzy, neural, etc.). The only condition of this
                 approach is that there exists a simulation model of the
                 designed closed-loop. In the future, this design
                 approach will be extended for solving very complex
                 design tasks in the process control area like complex
                 MIMO control systems for non-linear continuous-time
                 systems and for robotic applications using parallel
                 evolutionary algorithms.",
  size =         "22 pages",

Genetic Programming entries for Ivan Sekaj