In order to improve the security and compliance of physical human-robot interaction (pHRI), an adaptive fuzzy impedance control for robotic manipulators based on finite-time command filtered method is proposed in this paper. Firstly, robots usually encounter system uncertainties in practical applications, and the adaptive fuzzy control is introduced to approximate the system uncertainties. Secondly, the finite-time control method is used to improve the interaction performance of the system. Then, the command filtered control technique is used to deal with the "computational complexity " of traditional backstepping. Finally, simulations are conducted to illustrate the effectiveness of the proposed control method in physical human-robot interaction.
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.
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