Adaptive neural network command filtered backstepping impedance control for uncertain robotic manipulators with disturbance observer

Abstract

In this paper, an adaptive neural network command filtered backstepping impedance control method is developed for uncertain robotic manipulators with disturbance observer. First, an adaptive neural network algorithm is used to estimate the uncertain dynamics in the robot system. Second, impedance control is introduced to adjust the force and position relationship in physical human–robot interaction (pHRI). Third, a disturbance observer is employed to estimate the unknown external disturbance in the environment and compensate the control system to improve the safety of pHRI. Then, the command filtered technique can overcome problems of the ‘computational complexity’ and ‘singularity’ of traditional backstepping design. Finally, the simulation results are provided to illustrate the effectiveness of the proposed control method in pHRI.