Abstract
This paper presents an adaptive robust fuzzy control architecture for robot manipulators. The control objective is to adaptively compensate for the unknown nonlinearity of robot manipulators which is represented as a fuzzy rule-base consisting of a collection of if-then rules. The algorithm embedded in the proposed architecture can automatically update fuzzy rules and, consequently it is guaranteed to be globally stable and to drive the tracking errors to a neighborhood of zero. Focusing on realization, hardware limitations such as traditional long computation time and excessive memory-space usage are also relaxed by incorporating heuristic concepts, which reveals the flexible feature of this architecture. The present work is applied to the control of a five degree-of-freedom (DOF) articulated robot manipulator. Experiment results show that the proposed control architecture features fast convergence.
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