Sliding Mode Fuzzy Controller Applied to Robot Manipulator

Abstract

Sliding Mode Fuzzy Controller (SMFC) which has sliding surface gains is on-line tuned by minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories tracking between the robot manipulator actual and desired trajectory. An educational simulation tool objective is to make practical teaching, learning kinematics and dynamic modeling and to apply different controllers on robot manipulator. Pure Sliding Mode Controller (SMC) and Sliding Mode Fuzzy Controller have difficulty in handling unstructured model uncertainties. It is possible to solve this problem by combining sliding mode fuzzy controller and fuzzy-based tuning. Since the sliding surface gain is adjusted by fuzzy based tuning method, the sliding surface slope updating factor of fuzzy-based tuning part can be changed with the changes in error and change of error rate between half to one. Sliding surface gain is adapted on-line by sliding surface slope updating factor. In pure sliding mode controller and sliding mode fuzzy controller, the sliding surface gain is chosen by trial and error, which means that pure sliding mode controller and sliding mode fuzzy controller must have a prior knowledge of the system uncertainty. Fuzzy-based tuning sliding mode fuzzy controller is a model-free stable control for robot manipulator. It is a one of the best solution to eliminate chattering phenomenon with switching function in structure and unstructured uncertainties.