Flight Control System Design Optimisation via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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