Control System Design Optimisation via Genetic Programming

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

@InProceedings{Bourmistrova:2007:cec,
  author =       "A. Bourmistrova and S. Khantsis",
  title =        "Control System Design Optimisation via Genetic
                 Programming",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "1993--2000",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1691.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424718",
  abstract =     "This paper describes a stochastic approach for
                 comprehensive diagnostics and validation of control
                 system architecture for Unmanned Aerial Vehicle (UAV).
                 Mathematically based diagnostics of a 6 DoF system
                 provides capability for a complex evaluation of system
                 components behaviour, but are typically both memory and
                 computationally expensive. Design and optimisation of
                 the flight controllers is a demanding task which
                 usually requires deep engineering knowledge of
                 intrinsic aircraft behaviour. Evolutionary Algorithms
                 (EAs) are known for their robustness for a wide range
                 of optimising functions, when no a priori knowledge of
                 the search space is available. Thus it makes
                 evolutionary approach a promising technique to design
                 the task controllers for complex dynamic systems such
                 as an aircraft. In this study, EAs are used to design a
                 controller for recovery (landing) of a small fixed-wing
                 UAV on a frigate ship deck. The control laws are
                 encoded in a way common for Evolutionary Programming.
                 However, parameters (numeric coefficients in the
                 control equations) are optimised independently using
                 effective Evaluation Strategies, while structural
                 changes occur at a slower rate. The fitness evaluation
                 is made via test runs on a comprehensive 6
                 degree-of-freedom non-linear UAV model. The need of a
                 well defined approach to the control system validation
                 is dictated by the nature of UAV application, where the
                 major source of mission success is based on autonomous
                 control system architecture reliability. The results
                 show that an effective controller can be designed with
                 little knowledge of the aircraft dynamics using
                 appropriate evolutionary techniques. An evolved
                 controller is evaluated and a set of reliable algorithm
                 parameters is validated.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Anna Bourmistrova Sergey Khantsis

Citations