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This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algo- rithms (FNNA). With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertain- ties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.
This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algo- rithms (FNNA). With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertain- ties are eliminated. FNNA is used to handle model uncertainties In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.