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. 1 Introduction Control of robotic systems poses many challenging problems, particularly in unstructured environments. At present, the majority of robots used in industry operate in structured environments and perform simple repetitive tasks that require only end-effector positioning or motion along fixed paths. However, operations in unstructured environments require robots to perform m...