Neural Network-Based Region Tracking Control for a Flexible-Joint Robot Manipulator

The present paper proposes a neural network-based adaptive region-tracking control strategy for a flexible-joint robot manipulator subjected to region constraints. The developed neural network-based control strategy can globally stabilize the robot manipulator and cope with model uncertainties and the external unknown bounded disturbances. Different from the existing literature, by using the sliding mode technology and the singular perturbation theory, the developed control strategy does not require the high-order derivatives of the link states such as jerk and acceleration since the high-order derivative information is not always available in practical applications. By using Lyapunov stability theory, it is proved that the proposed neural network-based control strategy can guarantee that all the parameter variables in the closed-loop system are bounded, and the flexible-joint robot manipulator with unknown dynamics can reach inside the dynamic region and also maintain the velocity matching with the desired moving region. Since the assumption of linearization of the unknown dynamic parameters is removed, the proposed control strategy does not require the calculation of the complex regression matrix. Therefore, the proposed method has great robustness and the ability of model generalization. Simulations are given to demonstrate the validity of the proposed control strategy.