Hybrid fuzzy-control schemes for robotic systems

Abstract

Three hybrid fuzzy control schemes for robotics applications are described. The first scheme concentrates on a control architecture which incorporates fuzzy logic theory into the framework of behavior control for mobile robot navigation. The second scheme develops a two-level hierarchical fuzzy control structure for flexible manipulators. It incorporates genetic algorithms (GA) in a learning scheme to adapt to various environmental conditions. The third scheme concentrates on a methodology that uses a neural network (NN) to adapt a fuzzy logic controller (FLC) in manipulator trajectory following tasks.