| Authors: |
Abdollah Homaifar
and Daryl Battle
and Edward Tunstel
|
| URL: |
http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587 |
| Tags: |
Darwinian
algorithms,
automatic
autonomous
bases,
computation,
computing-based
concepts,
control
design,
evolutionary
forward
functions,
fuzzy
genetic
learning,
mappings,
measurement
membership
mobile
noise,
nominal
nonlinear
path
problem,
programming,
robot
robot,
robustness,
rule
rules,
sensor
soft
steering
tracking,
vehicle,
velocity
|
| Abstract: |
A variety of evolutionary algorithms, operating
according to Darwinian concepts, have been proposed to
approximately solve problems of common engineering
applications. Increasingly common applications involve
automatic learning of nonlinear mappings that govern
the behavior of control systems. In many cases where
robot control is of primary concern, the systems used
to demonstrate the effectiveness of evolutionary
algorithms often do not represent practical robotic
systems. In this paper, genetic programming (GP) is the
evolutionary strategy of interest. It is applied to
learn fuzzy control rules for a practical autonomous
vehicle steering control problem, namely, path
tracking. GP handles the simultaneous evolution of
membership functions and rule bases for the fuzzy path
tracker. As a matter of practicality, robustness of the
genetically evolved fuzzy controller is demonstrated by
examining the effects of sensor measurement noise and
an increase in the robot's nominal forward velocity. |
@inproceedings{Homaifar:1999:CIRA,
title = {Soft computing-based design and control for mobile
robot path tracking},
author = {Abdollah Homaifar and Daryl Battle and Edward Tunstel},
booktitle = {Computational Intelligence in Robotics and Automation,
CIRA '99. Proceedings. 1999 IEEE International
Symposium on},
month = {8-9 November},
pages = {35--40},
url = {http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587},
year = {1999},
abstract = {A variety of evolutionary algorithms, operating
according to Darwinian concepts, have been proposed to
approximately solve problems of common engineering
applications. Increasingly common applications involve
automatic learning of nonlinear mappings that govern
the behavior of control systems. In many cases where
robot control is of primary concern, the systems used
to demonstrate the effectiveness of evolutionary
algorithms often do not represent practical robotic
systems. In this paper, genetic programming (GP) is the
evolutionary strategy of interest. It is applied to
learn fuzzy control rules for a practical autonomous
vehicle steering control problem, namely, path
tracking. GP handles the simultaneous evolution of
membership functions and rule bases for the fuzzy path
tracker. As a matter of practicality, robustness of the
genetically evolved fuzzy controller is demonstrated by
examining the effects of sensor measurement noise and
an increase in the robot's nominal forward velocity.},
isbn = {0-7803-5806-6}, notes = {CIRA'99 http://web.nps.navy.mil/~yun/cira99/}, size = {6 pages},
keywords = {Darwinian algorithms, automatic autonomous bases, computation, computing-based concepts, control design, evolutionary forward functions, fuzzy genetic learning, mappings, measurement membership mobile noise, nominal nonlinear path problem, programming, robot robot, robustness, rule rules, sensor soft steering tracking, vehicle, velocity }
}
::