Abstract

This paper proposes an adaptive neural impedance control (ANIC) strategy for electrically driven robotic systems, considering system uncertainties and external disturbances. For the considered robotic system, the joint velocities and armature currents are assumed to be unknown and unmeasured, and an adaptive observer is then designed to estimate its unknown states using a neural network. Based on the observed joint velocities and armature currents, an ANIC scheme is proposed and the performances of the joint positions and force tracking can be improved. We also prove that the control system is stable and all the signals in closed-loop system are bounded. Simulation examples on a two-link electrically driven robotic manipulator are presented to show the effectiveness of the proposed observer-based intelligent impedance control method.

中文翻译：

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基于神经自适应观察器的电动机器人系统的自适应神经阻抗控制

摘要

考虑到系统的不确定性和外部干扰，本文针对电动机器人系统提出了一种自适应神经阻抗控制（ANIC）策略。对于所考虑的机器人系统，假定关节速度和电枢电流未知且未测量，然后设计自适应观察器以使用神经网络估计其未知状态。基于观察到的关节速度和电枢电流，提出了一种ANIC方案，可以改善关节位置和力跟踪的性能。我们还证明了控制系统是稳定的，并且闭环系统中的所有信号都是有界的。给出了两链电动机器人操纵器上的仿真示例，以显示所提出的基于观察者的智能阻抗控制方法的有效性。