Robust Adaptive Tracking Control for Manipulators Based on a TSK Fuzzy Cerebellar Model Articulation Controller

Abstract

The robot manipulator system is a complicated system with multiple-input and multiple-output, high nonlinearity, strong coupling, and uncertainties, such as parameter disturbances, external interference, and unmodeled dynamics. A robust adaptive Takagi-Sugeuo-Kang fuzzy cerebellar model articulation controller (RATFC) is proposed and applied to a robot manipulator to achieve high-precision position and speed control. A Takagi-Sugeuo-Kang fuzzy cerebellar model articulation controller is adopted, and the parameters are regulated by the derived adaptable rules according to a Lyapunov function. The robust compensation controller mitigates approximation-based errors. Finally, simulation results show that the proposed RATFC can achieve improved tracking performance compared with other neural network controllers.