Title :
Soft computing paradigms for learning fuzzy controllers with applications to robotics
Author :
Tunstel, E. ; Akbarzadeh-T, M.-R. ; Kumbla, K. ; Jamshidi, M.
Author_Institution :
Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA
Abstract :
Three soft computing paradigms for automated learning in robotic systems are briefly described. The first employs genetic programming to evolve rules for fuzzy behaviors to be used in mobile robot control. The second paradigm develops a two-level hierarchical fuzzy control structure for flexible manipulators. It incorporates genetic algorithms in a learning scheme to adapt to various environmental conditions. The third paradigm concentrates on a methodology that uses a neural network to adapt a fuzzy logic controller in manipulator control tasks. Simulation results of fuzzy controllers learned with the aid of these soft computing paradigms are presented
Keywords :
fuzzy control; fuzzy neural nets; genetic algorithms; hierarchical systems; intelligent control; learning systems; manipulators; mobile robots; neurocontrollers; optimal control; automated learning; environmental conditions adaptation; flexible manipulators; fuzzy behavior; fuzzy logic controller; genetic algorithms; genetic programming; manipulator control tasks; mobile robot control; neural network; robotic systems; rule evolution; simulation; soft computing paradigms; two-level hierarchical fuzzy control structure; Automatic control; Fuzzy control; Fuzzy logic; Genetic algorithms; Genetic programming; Manipulators; Mobile robots; Neural networks; Robot control; Robotics and automation;
Conference_Titel :
Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American
Conference_Location :
Berkeley, CA
Print_ISBN :
0-7803-3225-3
DOI :
10.1109/NAFIPS.1996.534759
