Adaptive neural network force tracking impedance control for uncertain robotic manipulator based on nonlinear velocity observer

Abstract

In this paper, an adaptive neural network force tracking impedance control scheme based on a nonlinear observer is proposed to control robotic system with uncertainties and external disturbances. It is supposed that the joint positions and interaction force of the robotic system can be measured, while the joint velocities are unknown and unmeasured. Then, a nonlinear velocity observer is designed to estimate the joint velocities of the manipulator, and the stability of the observer is analyzed using the Lyapunov stability theory. Based on the estimated joint velocities, an adaptive radial basis function neural network (RBFNN) impedance controller is developed to track the desired contact force of the end-effector and the desired trajectories of the manipulator, where the adaptive RBFNN is used to compensate the system uncertainties so that the accuracy of the joint positions and force tracking can be then improved. Based on the Lyapunov stability theorem, it is proved that the proposed adaptive RBFNN impedance control system is stable and the signals in closed-loop system are all bounded. Finally, simulation examples on a two-link robotic manipulator are presented to show the efficiency of the proposed method.