Hybrid neural network and variable structure control for robot

This paper proposes a neural-network and continuous sliding mode hybrid control method for robot manipulators, which has a unknown model. First, a feedforward neural network is used to learn the characteristics of the robot system (or specially its inverse dynamics) for accurate trajectory following and smooth torque control. Then, a saturated-function-based continuous sliding mode controller is used to guarantee the convergence of the tracking errors, reduces or even eliminates the chattering. Simulations of a two-link robot are given to illustrated the good transient performance and smooth control torque