By Topic

Combination of fuzzy logic and neural networks for the intelligent control of micro robotic systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wohlke, G. ; Inst. for Real-Time Comput. Syst. & Robotics, Karlsruhe Univ., Germany ; Fatikow, S.

The authors present an advanced control concept for microrobotic systems, which is based on the combination of a neural network approach for the adaptation of manipulation parameters and a fuzzy logic approach for the correction of parameter values given to a conventional controller. This multilevel system architecture is suitable for the intelligent control of microrobots that can operate autonomously in changing environments. Typical tasks for these robots are exploration and fine manipulation, which demand intelligent task planning and motion/force control capabilities. The planning component deals with the successive determination of initial manipulation parameters, whereas the neural system performs during manipulation, computing suboptimal grasp forces and learning inference rules used for parameter adjustment

Published in:

Intelligent Robots and Systems '93, IROS '93. Proceedings of the 1993 IEEE/RSJ International Conference on  (Volume:3 )

Date of Conference:

26-30 Jul 1993