Neural network–based terminal sliding mode applied to position/force adaptive control for constrained robotic manipulators

Abstract

This study considers the control problem of constrained robotic manipulators with dynamic uncertainties. A new force/position control strategy is proposed based upon terminal sliding mode and neural network. The terminal sliding mode combines position tracking with velocity tracking, and the neural network estimates unknown dynamics. Then, an adaptive control law is utilized to ensure finite-time convergent performance of position tracking and boundedness of contacting force tracking. Compared with existing force/position control strategies, the proposed strategy ensures the convergent performance without nominal model of the system dynamics. Simulation analysis verifies that the proposed strategy is effective.