Flight Control System Design Optimisation via Genetic Programming

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

@InCollection{Bourmistrova:2009:AV,
  author =       "Anna Bourmistrova and Sergey Khantsis",
  title =        "Flight Control System Design Optimisation via Genetic
                 Programming",
  booktitle =    "Aerial Vehicles",
  publisher =    "InTech",
  year =         "2009",
  editor =       "Thanh Mung Lam",
  chapter =      "7",
  keywords =     "genetic algorithms, genetic programming, mobile
                 robotics",
  isbn13 =       "978-953-7619-41-1",
  URL =          "http://www.intechopen.com/download/pdf/pdfs_id/5969",
  bibsource =    "OAI-PMH server at www.intechopen.com",
  language =     "eng",
  oai =          "oai:intechopen.com:5969",
  URL =          "http://www.intechopen.com/articles/show/title/flight_control_system_design_optimisation_via_genetic_programming",
  DOI =          "doi:10.5772/6470",
  abstract =     "In this chapter, an application of the Evolutionary
                 Design (ED) is demonstrated. The aim of the design was
                 to develop a controller which provides recovery of a
                 fixed-wing UAV onto a ship under the full range of
                 disturbances and uncertainties that are present in the
                 real world environment. The controller synthesis is a
                 multistage process. However, the approach employed for
                 synthesis of each block is very similar. Evolutionary
                 algorithm is used as a tool to evolve and optimise the
                 control laws. One of the greatest advantages of this
                 methodology is that minimum or no a priori knowledge
                 about the control methods is used, with the synthesis
                 starting from the most basic proportional control or
                 even from `null' control laws. During the evolution,
                 more complex and capable laws emerge automatically. As
                 the resulting control laws demonstrate, evolution does
                 not tend to produce parsimonious solutions. The method
                 demonstrating remarkable robustness in terms of
                 convergence indicating that a near optimal solution can
                 be found. In very limited cases, however, it may take
                 too long time for the evolution to discover the core of
                 a potentially optimal solution, and the process does
                 not converge. More often than not, this hints at a poor
                 choice of the algorithm parameters. The most important
                 and difficult problem in Evolutionary Design is
                 preparation of the fitness evaluation procedure with
                 predefined special intermediate problems. Computational
                 considerations are also of the utmost importance.
                 Robustness of EAs comes at the price of computational
                 cost, with many thousands of fitness evaluations
                 required. The simulation testing covers the entire
                 operational envelope and highlights several conditions
                 under which recovery is risky. All environmental
                 factors--sea wave, wind speed and turbulence--have been
                 found to have a significant effect upon the probability
                 of success. Combinations of several factors may result
                 in very unfavourable conditions, even if each factor
                 alone may not lead to a failure. For example, winds up
                 to 12 m/s do not affect the recovery in a calm sea, and
                 a severe ship motion corresponding to Sea State 5 also
                 does not represent a serious threat in low winds. At
                 the same time, strong winds in a high Sea State may be
                 hazardous for the aircraft.",
  size =         "34 pages",
}

Genetic Programming entries for Anna Bourmistrova Sergey Khantsis

Citations