Adaptive Fuzzy Tracking Control for Robotic Manipulators with Adjustable Gains

In this paper, an adaptive fuzzy scheme for trajectory tracking is developed for robotic systems with parameter uncertainties and external disturbance. Within the scheme, a fuzzy logic system is employed to approximate the unknown robotic dynamics. Then, an adaptive fuzzy controller is obtained which combine with the adaptive methods so as to eliminate the system uncertainties and external disturbance. And the adaptive laws are obtained through stable control algorithms based on the Lyapunov stability theorem. Moreover, extra control terms are designed to cancel the approximation errors, and the adjustable gains obtained from a Fuzzy Logic System (FLS) are considered to receive the satisfying control effects. The proposed controller is robust to structured uncertainties and unstructured uncertainties, and the validity of the control scheme is shown by simulations on a novel 2-DOF parallel mechanism, which is driven by linear motors.