ANFIS-based an adaptive continuous sliding-mode controller for robot manipulators in operational space

Abstract

This paper addresses the task-space robust trajectory tracking control problem for robot manipulators in the presence of uncertainties and external disturbances. First, a discontinuous sliding-mode controller-based inverse dynamics control strategy (IDSMC) with discontinuous robust control action is synthesized. Second, an adaptive inverse dynamics controller based on continuous sliding-mode control (AIDCSMC) is designed, in which the adaptation laws are addressed to compensate for the unknown parameters of the dynamical model of robot manipulators. The global stability of the closed-loop control system is proven using the Lyapunov theorem and the proposed AIDCSMC controller is further proven to guarantee convergence to zero of both trajectory tracking error and error rate. Finally, a hybrid intelligent neuro-fuzzy adaptive fuzzy inference system (ANFIS)-based adaptive inverse dynamics controller with continuous sliding-mode control (ANFIS-AIDCSMC) is adopted. Numerical simulations using the dynamic model of rigid robot manipulators with uncertainties show the effectiveness of the presented approach in simple and complex trajectory tracking problems. The simulation results indicate that the control performance of the robot system is satisfactory, and the proposed approach can achieve favorable tracking performance and it is robust with regard to uncertainties.