Genetic Programming Design of Fuzzy Controllers for Mobile Robot Path Tracking

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

@Article{Homaifar:2000:IJKBIES,

• author = "Abdollah Homaifar and D. Battle and E. Tunstel and G. Dozier",
• title = "Genetic Programming Design of Fuzzy Controllers for Mobile Robot Path Tracking",
• journal = "International Journal of Knowledge-Based Intelligent Engineering Systems",
• year = "2000",
• volume = "4",
• number = "1",
• pages = "33--52",
• month = jan,
• keywords = "genetic algorithms, genetic programming",
• abstract = "Genetic programming (GP) is an evolutionary strategy that attempts to deal with the notion of how computers can learn to solve problems without being explicitly programmed. It has been demonstrated that GP, under the influence of Darwinian concepts, could genetically breed computer programs to approximately solve problems in a variety of applications. One primary example is its application to the problem of automatically learning nonlinear mappings that govern the behavior of control systems. It is demonstrated here that GP can formulate such nonlinear maps in the form of fuzzy control rules, which yield comparable or better performance than one derived through manual design using trial-and-error. The objective is to address the efficient implementation of GP for the discovery of knowledge bases intended for use in fuzzy logic controller applications. Efficiency is achieved with a C programming language implementation of GP, which is applied to a mobile robot steering control problem. Robot path following performance is compared to results obtained using an existing GP implementation in the LISP programming language. It is demonstrated that the C implementation has a definite advantage with regard to computational speed of evolution. In this work, we have extended the application of GP to handle simultaneous evolution of membership functions and rule bases for the same control problem. Furthermore, GP is used to handle selection of fuzzy t-norms. It is concluded that simultaneous evolution of rule bases and membership functions with t-norm selection results in enhanced performance of the evolved controllers. Finally, the robustness characteristics of the genetically evolved fuzzy controllers are investigated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity.",
• notes = "Nov 2012 IJKBIES web site not listing stuff before 2004",

}

Genetic Programming entries for Abdollah Homaifar Daryl Battle Edward W Tunstel Gerry Dozier

Citations