Adaptive neural backstepping control for flexible-joint robot manipulator with bounded torque inputs

Abstract

Aiming at tracking control with bounded torque inputs of the flexible-joint robot manipulators, we propose a generalized saturated adaptive controller based on backstepping control, singular perturbation decoupling and neural networks. First, by using the singular perturbation theory, the full-order rigid-flexible dynamics of the robot manipulator is decoupled into a slow subsystem and a fast subsystem. Second, saturated sub-controller by backstepping method is proposed for the slow subsystem, where the projection-type parameter adaptation and a class of saturation functions are applied to make the torque inputs bounded, and a saturated neural network approximator is involved to simplify the control law and to compensate for the uncertain nonlinearity. Third, for fast subsystem, a new filtered tracking error of the elastic torque is used in the fast control law to make the boundary layer subside quickly. In addition, explicit but strict stability analysis is given for the system. Finally, comparisons indicate that the proposed controller results in a more satisfactory tracking performance with keeping the control inputs bounded within the given range all the time and superior anti-disturbance capability.