A neuro-fuzzy logic controller for trajectory tracking of uncertain robots

Abstract

This paper presents an adaptive fuzzy computed-torque controller, that enhances fuzzy controllers with an embedded adjustable two-stages credit assignment and self-learning capability, for uncertain robots, to online track a prescribed trajectory. An adaptation law for the parameters of controller is combined with the dead-zone technique to guarantee a given attenuation region of tracking error in the presence of torque disturbance. Simulations of a two-link robot carrying a heavy load illustrate the effectiveness and attenuation capability of the controller for online trajectory tracking in the presence of inertial parameters uncertainties and torque disturbances.